Information processing analysis system for sorting and scoring text

ABSTRACT

A method and system for text analysis provides that text messages perceived by a population can be scored to determine the extent to which the messages favor one or more specified positions on a specified issue. A method and system for predicting public opinion based on message scores provides that the extent to which messages favor one or more specified positions can be used to determine the effect on the opinions of a specified population and to determine changes in the percentages of the percent of subpopulations within said specified population which favor said one or more specified positions.

This is a continuation-in-part division of application Ser. No.07/529,101, filed May 25, 1990, now abandoned, which is a continuationdivision of application Ser. No. 07/35,080, filed Apr. 6, 1987, now U.S.Pat. No. 4,930,077.

TECHNICAL FIELD OF THE INVENTION

This invention generally concerns an expert system in artificialintelligence in which a computer performs scoring of informationavailable to the public and uses such scores to predict expected publicopinion.

BACKGROUND OF THE INVENTION

One of the major areas of this invention is the computer analysis oftext. Here, "text" refers to a stream of data bits. Individual data bitswill be referred to as "characters" and will typically be ASCII(American Standard Code for Information Exchange) characters. Smallstrings of such data bits are grouped into units called "words" witheach word having a small number of defined meanings. Unless otherwisestated, words need not begin and end with special characters likespaces, carriage returns or line feeds.

In some expert systems for computer text analysis, words in text areassigned to defined categories and the number words in each category iscounted. Final interpretations are based on the frequencies of words indifferent categories. Although this strategy is suitable for analyzinglarge quantities of text ranging to millions of characters or more,meanings due to relationships between words are lost.

Other expert systems include the spatial relationships between words inthe text analysis. This strategy has the disadvantage that only limitedamounts of text can be examined without the rules for assessing wordrelationships becoming exceedingly complex and very time consuming tocompute.

In addition, no general and systematic method has been proposed ineither of these areas of artificial intelligence to obtain directlynumerical scores for the extent to which a "story" supports different"positions" for an "issue." Here, an issue is basically a question suchas: should there be More, Same, or Less spending for military defense. Aposition, also called an "idea," is a possible answer such as: More,Same, Less or Don't know. A story is a continuous segment of text.

The other major area of this invention concerns the determination ofexpected public opinion, and, more generally, the determination ofexpected social traits. Previous attempts to develop an artificialintelligence system for predicting public opinion are described in twoprevious publications: "Mathematical models for the impact ofinformation on society," by David P. Fan in Political Methodology, Vol.10, pp. 479-493, 1984; and "Ideodynamics: The kinetics of the evolutionof ideas," by David P. Fan in Journal of Mathematical Sociology, Vol.11, pp. 1-23, 1985. This system was based on a model calledideodynamics.

In ideodynamics, expected opinion is computed from scores for the extentto which information supports different positions. Expected opinion isdetermined as a time trend with the time intervals of the determinationsbeing of arbitrarily small size.

For the opinion computations in the prior system, each persuasivemessage able to change minds of people in a population was defined as an"infon" I_(ijk) where i=1,2 was the index referring to whether the infonwas available to all people (i=1) or only those already aware of theissue (i=2) supported a position, where j=1,2 was the index referring tothe position (denoted by Q_(j)) the infon favored, and where k was theindex identifying the individual message. Each infon was defined to havethe characteristics of t_(ijk) =the time the infon first arrived at thepopulation, a_(ijk) =the fraction of the audience reached immediatelyafter the infon was sent, v_(ijk) =the credibility of the infon, c_(ijk)=the fraction of the infon favoring idea Q_(j) and d_(ijk) (t)=functiondescribing the fraction of the population accessing the infon at time t.

For opinion formation, equations describing the effects of infons fromthe mass media on the population were ##EQU1## where p_(ijk) isconstant, ##EQU2## for all k with t_(ijk) <t, ##EQU3## where A(t') isthe fraction of the population aware of the issue at time t', ##EQU4##for all k with t_(ijk) <t,

    H.sub..j =G.sub..j /(G.sub... +w)                          (6)

where w is a weighting constant, and

    (dB/dt=k.sub.2 (1-B).H.sub..1 -k.sub.2 B.H.sub..2          ( 7)

where B is the fraction of the population believing in position Q₁ givenby subscript j=1 assuming that there was only one other possibleposition (Q₂) opposed to idea Q₁.

The previous ideodynamic system suffered from several drawbacksrendering it inoperable. First, an infon was defined as "a single packetof information transmitted in identical copies to a group of people"(the article by Fan in the Journal of Mathematical Sociology, 1985,described above). Since each infon is defined as I_(ijk) with only oneindex j, the implication is that an infon can only support the singleposition Q_(j). However, in defining content scores c_(ijk), there isthe implication that an infon can have contents supporting more than oneposition. Therefore, there is contradiction in the terms definedearlier.

In the system of this invention, "a single packet of informationtransmitted in identical copies to a group of people" is redefined as a"persuasive message" with an "infon" I_(ijk) now referring to "acomponent of a message favoring position or idea Q_(j)." In fact, it ispossible to divide a message into several infons all favoring the sameposition Q_(j). All such infons would have the same subscript j butwould have different subscripts i and/or k.

Second, all persuasive messages were assumed to have the same totalcontent score since the content score c_(ijk) for any one position wasonly the fraction of the message favored by infon I_(ijk). Thisinterpretation gives excessive weights to persuasive messages with verylittle information relevant to the issue. Therefore, the result will behighly inaccurate opinion determinations.

In this invention, c_(ijk) is redefined as the total and not thefractional content of infon I_(ijk) favoring position Q_(j).

Third, the prior equation for opinion determination (equation 7) did notinclude the case of an issue having more than the two positions of proand con.

In this invention, a new equation is used where any number of positionsis possible. The extension to more positions could have resulted from anumber of different assumptions so the formulation in this inventioncannot be directly deduced from the prior system.

Fourth, the equations in the systems of this invention no longer includethe term d_(ijk) (t). Also, the constant a_(ijk) is redefined as a_(ijk)(t)=a function of time including features from both a_(ijk) and d_(ijk)(t) in the previous formations of equations 1-7.

Fifth, equation 6 is now replaced by a totally new equation in whichconstant w is eliminated and in which G.sub... no longer appears. Thereplacement equation is not a natural extension of equation 6 since thenew constants have no relation to constant w in equation 6. With thisinvention no longer using constant w, the entire sketch for the solutionof equations 1-7 in the prior system is inoperative since that sketchrequired finding constant w.

Sixth, the prior system did not permit messages favoring differentpositions to have different abilities to cause opinion change. Thispossibility is now included by the introduction of constants w_(ij'j")(see equation A.29 of step III-4 of the Preferred Embodiment below).

Seventh, the prior system did not specify the method for solvingdifferential equation 7 including the setting of the boundary conditionsso that system did not give a complete description of the opiniondetermination.

SUMMARY OF THE INVENTION

Disclosed is a system for using an electronic computer to determineexpected social traits, and in particular, public opinions for thespecified positions of a specified issue based on information availableto the public. The three basic stages of the system involve: thegathering of representative messages relevant to the issue over a periodof time, the generation of numerical scores for the messages, and theuse of the scores to determine time trends of public opinion throughoutthe time period for which the messages are available. The preferredmessages are in the form of text which can be retrieved from electronicdata bases. The system first provides a method to score text using anelectronic computer. The system then determines expected opinion fromchanges in opinion due to the influence of the messages as representedby their numerical scores.

BRIEF DESCRIPTION OF THE DRAWINGS

FIG. 1 is a perspective view of the electronic computer and peripheraldevices used for this invention.

FIG. 2 is a simplified block diagram of an alternative hardware logicembodiment of the invention.

FIG. 3 is a logical flow diagram showing the functional operation of thealternative embodiment.

FIG. 3.1 illustrates polled data for defense spending.

FIG. 4.1 illustrates examples of persuasive forces of infons.

FIG. 4.2 illustrates a population conversion model for defense spending.

FIG. 4.3 illustrates the impact of persuasive infons favoring moredefense spending.

FIG. 4.4 illustrates persuasive forces of AP infons scored for favoringmore, same and less defense spending.

FIG. 4.5 illustrates persuasive forces of AP infons scored for favoringmore and less defense spending.

FIG. 4.6 illustrates opinion on defense spending from dispatches scoredto favor more, same and less spending.

FIG. 4.7 illustrates constant optimization curves for defense spending.

FIG. 4.8 illustrates opinion from a subset of AP dispatches scored tofavor more, less or more, same and less defense spending.

FIG. 4.9 illustrates opinion from another subset of AP dispatches scoredto favor more, same and less defense spending.

FIG. 4.10 illustrates opinion on defense spending assuming the entirepopulation favored more spending of the same at the time of the firstscored AP infons in January 1997.

FIG. 4.11 illustrates opinion on defense spending from dispatches scoredto favor more and less defense spending only.

FIG. 4.12 illustrates persuasive forces of AP infons from 1977 to 1986scored for favoring more, same and less defense spending.

FIG. 4.13 illustrates opinion on defense spending for 1977 and 1986.

FIG. 4.14 illustrates constant optimization curve for the contributionsfrom paragraphs favoring less defense spending.

FIG. 4.15 illustrates effective stories on waste and fraud on publicopinion on defense spending.

FIG. 4.16 illustrates persuasive forces for troops in Lebanon from APinfons only.

FIG. 4.17 illustrates persuasive forces for troops in Lebanon from APinfons without a truck bombing infons favoring more troops.

FIG. 4.18 illustrates population conversion model for actions of infonsfavoring more, same and less troops in Lebanon.

FIG. 4.19 illustrates optimization for the modified persuasabilityconstant, a wait for paragraphs favoring less troops and the value ofthe truck bombing infons favoring more troops.

FIG. 4.2 illustrates optimization curves for the persistence half-lifeand the value of the truck bombing infons favoring less troops.

FIG. 4.21 illustrates opinion on troops in Lebanon assuming only APinfons.

FIG. 4.22 illustrates opinion on troops in Lebanon with a truck bombinginfons favoring more troops.

FIG. 4.23 illustrates comparisons of opinion projections with (solidline) or without the truck bombing infons (dotted line) favoring moretroops.

FIG. 4.24 illustrates persuasive forces AP infons with and without atruck bombing infons favoring less troops.

FIG. 4.25 illustrates opinion projections with and without a truckbombing infons favoring less troops.

FIG. 4.26 illustrates persuasive forces favorable to Democraticpresidential candidates from AP paragraph scored using bandwagon words.

FIG. 4.27 illustrates persuasive forces unfavorable to Democraticpresidential candidates from AP paragraph scored using bandwagon words.

FIG. 4.28 illustrates persuasive forces of AP infons scored by name,only.

FIG. 4.29 illustrates population conversion model for actions of infonsscored using bandwagon words.

FIG. 4.30 illustrates population conversion model for actions of infonsscored by name, only.

FIG. 4.31 illustrates optimization curves for constants for Democraticprimary bandwagon analysis.

FIG. 4.32 illustrates opinion on Democratic candidates when infons werescored by the bandwagon analysis.

FIG. 4.33 illustrates opinion on Democratic candidates when infons werescored by name only.

FIG. 4.34 illustrates persuasive forces from AP paragraphs favoringbetter, same and worse economic conditions.

FIG. 4.35 illustrates population conversion model for actions of infonsfavoring better, same and worse economic conditions.

FIG. 4.36 illustrates optimization curves for constants for the economicclimate.

FIG. 4.37 illustrates opinion on economic climate.

FIG. 4.38 illustrates persuasive forces of AP infons favoringunemployment more important, equal importance and inflation moreimportant.

FIG. 4.39 illustrates a population conversion model for actions ofinfons favoring unemployment important, equal importance, and inflationmore important.

FIG. 4.40 illustrates optimization curves for the modifiedpersuasability constant and the infons waiting constants where theunemployment versus inflation paragraph.

FIG. 4.41 illustrates optimization curve for the persistence constantfor unemployment versus inflation.

FIG. 4.42 illustrates opinion favoring unemployment more important,equal importance or inflation more important.

FIG. 4.43 illustrates persuasive forces of AP infons scored by theauthor as favoring an opposing contra aid.

FIG. 4.44 illustrates persuasive forces of AP infons scored by the SwimMIENE and French as favoring an opposing contra aid.

FIG. 4.45 illustrates a population conversion model for actions ofinfons favoring opposing contra aid.

FIG. 4.46 illustrates constant optimization curves for contra aid.

FIG. 4.47 illustrates opinion favoring and opposing contra aid usinginfons scores by the author.

FIG. 4.48 illustrates opinion favoring and opposing contra aid usinginfons scores by the Swim MIENE and French.

DESCRIPTION OF THE PREFERRED EMBODIMENT

The invention will now be described as embodied in the computer systemshown in FIG. 1 with a human operator at the controls. This systemcomprises a computer 10, preferably compatible with the IBM-PCmicrocomputer, a printer 12, preferably the Hewlett-Packard Thinkjetprinter, and a modem 14. The printer 12 and modem 14 are attached to thecomputer 10 with the modem being further connected to remote devices bya telephone line. The computer comprises a keyboard by which theoperator can enter data, a display monitor for displaying information, adisk storage device, and a microprocessor with a random access memoryand a process execution unit. The microprocessor can store informationby writing the information to disks using the disk storage device. Themicroprocessor can then read the stored information using the diskstorage device. The computer can also send information to and receiveinformation from the modem.

A typical complete task as the invention is applied to determine publicopinion is now described with reference to FIG. 1. It will beunderstood, however, that the invention is also applicable to the moregeneralized task of determining social traits as will be discussedfurther below. In operation, the system operator passes commands anddata to the computer using the keyboard. The computer accomplishes thetask following commands given by the operator. Those commands includeinstructions for the computer to load and execute appropriate programsstored on disk. The data used by the computer come from the keyboard andfrom the modem. Usually, the computer will store the data on disk beforesubsequently reading that information from disk for the processingsteps.

The goal of the typical task is to use information in the AssociatedPress (AP) wire service to determine expected public opinion for anumber of specified positions all relating to a specified issue. Thebackground theory for this embodiment and additional details of thetechniques and results are presented in the book manuscript presented inthe last section of this patent, and referred to herein as Fan (1987).

The task is divided into three main sections discussed below in steps I,II, and III: Step I is to gather AP dispatches. Step II is to score thedispatches numerically for the extent to which different componentssupport different positions. Step III is to use these numerical scoresto determine public opinion.

I. Gathering of AP Dispatches

(I-1) In order to gather appropriate AP stories, it is first necessaryto define the issue for which opinion is to be determined and themutually exclusive positions within that issue. The positions aredefined so that an individual member of the population can have anopinion favoring only one of the defined positions. The possiblepositions are denoted by Q_(j) where j indexes the individual positions.

Consider, as example, the issue: "Should American defense spending beincreased, kept the same, or decreased?" The three positions within thisissue could be: should be increased, should be kept the same, should bedecreased. With three positions, j=1,2,3 in the term Q_(j). For brevity,it is convenient to refer to these three positions as Q₁ favoring Moredefense spending, Q₂ favoring Same defense spending, and Q₃ favoringLess defense spending. In actual polls, members of the public wereassigned by polling organizations as holding one of these threepositions (see Table B.1 of Fan, 1987).

In a addition to positions Q₁, Q₂, and Q₃, poll results also includedthe additional position of No Opinion typically comprising less than 10%of the population. With this small percentage, persons with No Opinionwere ignored. This action was equivalent to assuming that most of thesepersons did not participate in decision making on the issue andcontinued to have No Opinion. However, it would be possible to repeatthe analysis with No Opinion as yet another position.

(I-2) After the issue and its positions are defined, the system operatoruses the computer and modem to communicate with a remote storage device,also called a data base, containing the full texts of stories in the APwire service. One example is the Nexis data base sold by Mead DataCentral of Dayton, Ohio. The computer passes a search command to thedata base instructing the data base to identify all stories relevant tothe issue under analysis.

For the issue of defense spending analyzed in Fan (1987), the searchcommand was: (DEFENSE or MILITARY or ARMS) w/5 (BUDGET! or EXPENDITUREor SPEND! or FUND!) and date aft Jan. 1, 1977 and date bef Apr. 1, 1984.A full explanation of this command is given in Section B.1 of Fan(1987).

After receiving a search command, the Nexis data base responds with thetotal number of stories found. All stories are numbered in reversechronological order. The computer then chooses a series of randomnumbers with the length of the series being less than or equal to thetotal number of stories identified by the data base. The computer passesappropriate commands to the data base to retrieve the texts of thestories with numbers corresponding to those in the random number series.The computer retrieves the text using the modem and stores the materialon disk.

(I-3) The computer reads the retrieved text for a story from disk intomemory and edits the text to remove extraneous characters and to convertthe date into a "decimal date." For this conversion, it is useful tonote that all stories retrieved from the Nexis data base begin withintroductory material. This introductory text includes the date ofstory, preceded by a characteristic string of characters, usually theASCII characters ESCAPE-A1. The computer scans the text until thisstring is reached. Then the computer reads the next continuous string ofletters as the month (e.g. January), skips irrelevant characters, readsthe next integer as the date (e.g. 1), skips irrelevant characters, andthen reads the following integer as the year (e.g. 1987).

The computer checks the string of characters comprising the monthagainst an array of 12 words corresponding to the 12 months of the year.Upon finding a match, the computer adds the integer corresponding to thedate and the integer corresponding to the number of days in the yearbefore the beginning of the matching month. The computer divides theresulting sum by the total number of days in the year to get afractional year. The computer adds this fractional year to the last twodigits of the year to get the decimal date representing the date interms of the fraction of the year which had elapsed.

As an example, the decimal date for Jan. 1, 1987 would be 87.0027 where0.0027=(1+0)/365. Here, the date is 1 and the integer from the month is0 since there are no months before January.

The computer then continues to remove extraneous characters from thetext. These characters are not part of the text itself but are added bythe data base. For instance, the Nexis data base also sends the ASCIIcharacter string ESCAPE-A1 just prior to any word in the text whichcorresponded to a word in the search command (e.g. ARMS in the examplein step I-2).

Whenever three adjacent spaces are encountered, the computer replacesthe three spaces by the symbol "!" to indicate the beginning of aparagraph. When the computer reaches the end of a story, the computerwrites the edited story to disk. For this step, the computer writes inorder: the symbol "*" to indicate the beginning of the story, thedecimal date, and then the rest of the edited text.

The following is the edited text from a hypothetical AP story:

* 87.0027

!"Funding for the Marines and non-defense items should not be cut," hesaid.

!He did ask for a reduction in health care spending.

Optionally, the system operator examines the edited text and performs afinal manual editing to assure that all stories begin with the "*"symbol and the decimal date and that the paragraphs all being with the"!" symbol.

The editing is performed for every retrieved story. The result is a newset of files on disk containing the edited texts of all storiesretrieved from the data base.

II. Computer Assisted Content Analysis

Once relevant stories have been collected and edited in step I above,the task continues with computer text analysis to generate set ofnumerical scores. In outline, this analysis begins with a series of"text filtration" steps where the computer removes text irrelevant tothe opinions being determined. Once the text is sufficiently homogeneousfor scoring, the computer proceeds to the "text scoring" step. Thefiltration and scoring procedures are described in steps II-A and II-Bbelow, respectively.

II-A. Text Filtrations

(II-A1) Based on a random sample of edited stories, "filtrationcriteria" are developed. These criteria are used to formulate a "textanalysis dictionary" and a set of customized "text filtration rules" for"filtering text," that is, for discarding text irrelevant to the issuebeing analyzed. The text filtration rules are comprised of two parts: aset of "text transformation rules" and a set of "text filtration rules."The text analysis dictionary and filtration rules are stored on disk.

Consider, for example, the defense spending issue of step I-1 above. Forthis topic, it is possible to decide that only one filtration criterionis needed. This condition could be the removal of all paragraphs exceptthose relating directly to "American defense spending." This criterioncould be implemented using the text analysis dictionary and textfiltration rules those presented below. The rationales and compositionsof the dictionary entries and the rules will be discussed below.

(II-A2) The computer loads the text analysis dictionary from disk intomemory. The dictionary has two parts, a "concept array" and a"dictionary array."

The concept array lists all the essential concepts needed for the texttext analysis. Each element of the concept array is comprised of:

(a) a unique "concept symbol" made up of a single character in the ASCIIset of characters, and

(2) a unique "concept mnemonic" made up of a string of ASCII characterssufficiently long to suggest the meaning of the concept.

In the text filtration to select for paragraphs relevant to "Americandefense spending" (the criterion of step II-A1 above), the concept arraycould be Table 1.

                  TABLE 1                                                         ______________________________________                                        Concept array for text analysis dictionary                                    Concept mnemonic                                                                              Concept symbol                                                ______________________________________                                        AmericaWord     A                                                             DefenseWord     d                                                             SpendingWord    s                                                             AmerDefense     b                                                             AmerDefSpend    +                                                             NonPrefix       n                                                             ______________________________________                                    

The dictionary array of the text analysis dictionary is comprised of anumber of specified words. Each word is associated with one of theconcepts in the concept array. Therefore, each word has a correspondingconcept mnemonic and concept symbol.

Returning to the example of the text filtration for "American defensespending," the dictionary array might include the entries in Table 2.Here, only the concept symbol is given since the concept mnemonic can beread from Table 1.

                  TABLE 2                                                         ______________________________________                                        Dictionary array for text analysis dictionary                                 Dictionary word Concept symbol                                                ______________________________________                                        America         A                                                             U.S.            A                                                             defense         d                                                             funding         s                                                             budget          s                                                             Marine          b                                                             non             n                                                             ______________________________________                                    

In this example, the concept of "American" would have concept symbol "A"and concept mnemonic "AmericaWord." The words "America" and "U.S." couldboth belong to this concept. However, not every concept need havecorresponding words in the dictionary. For example, the concept of"American defense spending" could have concept symbol "+" together withconcept mnemonic "AmerDefSpend" and yet not be represented by any singleword.

(II-A3) Besides loading the dictionary, the computer loads the texttransformation rules from disk into memory. These rules are comprisedof:

(a) Initiation and termination signals for "blocks of text." Thesesignals are strings of characters. The computer scans for the firstoccurrence of an initiation signal after the decimal date. The computermarks this as the beginning of a block of text. The computer marks thefirst following termination signal as the end of the that block of text.The block of text itself is the text between the two signals.

For the sample edited text of step I-3 above, it is possible to discardirrelevant paragraphs in which case the appropriate blocks of text wouldbe paragraphs. Therefore, the "!" would be the only permissibleinitiation signal since all paragraphs in the edited text begin withthis symbol.

However, there would be two possible termination signals, the character"!" marking the beginning of a following paragraph and the end of storymarker described in step II-A5 below. Therefore, the "!" can mark boththe end of one block of text and the beginning of the next.

It is also possible to use other initiation and termination signals. Forexample, the "!" signal marking the beginnings of paragraphs could stillbe used as the initiation signal for discarding entire stories. In thiscase, the end of story maker would be the only permissible terminationsignal. Intervening appearances of "!" would not cause the block of textto end.

(b) Specified carryover symbols. These symbols are a subset of all theconcept symbols of step II-A2 above (see step II-A18 below for theiruse).

(c) Text equivalent transformation rules. These rules are a set ofindividual transformation rules (see step II-A15 below for their use).

(II-A4) Besides the text analysis dictionary and text transformationrules, the computer also loads the text filtration rules from disk intomemory. These rules are comprised of:

(a) a set of "text discard symbols" with each element comprised of aconcept symbol which would lead to discarding a block of text, and

(b) a set of "text retention symbols" with each element comprised of aconcept symbol which would lead to retaining a block of text.

(II-A5) After loading the dictionary and corresponding texttransformation rules, the computer loads the text of a specified storyinto memory, placing an end of story mark in the memory unit followingthe end of the story. In a disk file containing more than one story, the"*" symbol just before the decimal date of the next story (see exampleof step I-3) would delimit the end of the previous story. Alternatively,the end of the disk file would signal the end of a story.

(II-A6) The computer reads the "*" and decimal date at the beginning ofa story and writes this information to two separate disk files, a "fulltext output file" and a "filtered text output file." The computer thenscans the text until it encounters the first initiation signal andplaces a "current begin block of text" marker at the beginning of thissignal.

(II-A7) The computer also places a "current position" marker at the nextcharacter.

In the example in step I-3 above, the mark is on the quote signimmediately after the first "!".

(II-A8) The computer marks the first word in the text analysisdictionary as the "current dictionary word."

(II-A9) The computer prepares a "candidate word" comprising of a stringof characters in the text of the story beginning with the currentposition mark and extending into the text until the length currentdictionary word is reached or until a termination signal is encountered.The computer compares the candidate word with the current dictionaryword. In making the comparisons, all letters are reduced to the lowercase. Carriage returns and line feeds are considered to be theequivalent of a space.

(II-A10) If there is no match in step II-A9 above, the computer movesthe current dictionary word marker to the next word in the dictionaryand repeats step II-A9. This repetition is continued until either amatch is found or the end of the dictionary is reached.

(II-A11) If there is a match in either step II-A9 or step II-A10 above,the computer inserts into the text the following three characters justbefore the position of the match:

(a) the control character "<" indicating the arrival of a conceptsymbol,

(b) the concept symbol of the matching dictionary word, and

(c) the control character ">" indicating the end of the concept symbol.

(II-A12) After step II-A11, the computer advances the current positionmarker to the next character in the text and repeats steps II-A8 toII-A11. The computer continues this repetition until a terminationsignal is reached. The text between the current begin block of textmarker and this termination signal, including the character additions ofstep II-A11, is considered to be the "current block of text." At thispoint, the current block of text from the example of step I-3 above is:

!"<s>Funding for the <b>Marines and <n>non-<d>defense items should notbe cut," he said.

The computer writes the current block of text to the display monitor topermit the system operator to follow the progress of the text analysis.

(II-A13) The computer constructs a "text equivalent" for the currentblock of text comprised of an alternating series of elements: a"diagnostic symbol" and a "diagnostic distance:"

(a) The "diagnostic symbol" is comprised of either the symbol "*"denoting the beginning of the block of text or a concept symbol insertedin step II-A11 above.

(b) The "diagnostic distance" is an integer equivalent to the number ofcharacters between two concept symbols in the block of text, between aconcept symbol and the beginning or end of the block of text, or betweenthe beginning and end of the block of text if no concept symbols wereplaced in the block of text.

The computer constructs the text equivalent, stores it memory anddisplays it on the monitor as follows:

(a) The computer writes to the monitor the diagnostic symbol "*"indicating the beginning of the block of text. The computer then writesa space. At this point, the computer starts at the beginning of theblock of text, after the initiation signal, and counts characters up tobut not including the first "<" character or the termination signal forthe block of text, whichever is reached first. This number of charactersis the first diagnostic distance. The computer writes the diagnosticdistance followed by a space.

(b) If a "<" has been reached, the computer then writes the nextcharacter, the concept symbol of the following word, as next diagnosticsymbol. The computer again writes a space and skips the following ">"and counts characters to the next "<" or termination signal. Thecomputer then enters that number as the next diagnostic distance.

(c) Step b immediately above is then repeated until the terminationsignal is reached.

(d) The end of the text equivalent is delimited by a carriage returnfollowed by a line feed.

The text equivalent for the block of text in step II-A12 is:

* 1 s 16 b 12 n 4 d 42

(II-A14) The computer transforms the text equivalent based on the textequivalent of the previous block of text in a story. If there is noprior block, this step is skipped.

If there is a previous block, the computer consults the list of matchingcarryover symbols generated in step II-A16 below. The computer adds anymatching carryover symbols followed by the diagnostic distance of zeroto the end of the current text equivalent.

This step permits the implied meaning in a previous block of text to betransferred to the current block.

(II-A15) After any additions to the text equivalent in step II-A14, thethe computer makes transformations on the resulting text equivalentfollowing the text equivalent transformation rules of step II-A3. Eachrule is comprised of the following elements:

(a) a "specified operator symbol" which can be any one of the conceptsymbols in the text analysis dictionary,

(b) a "specified target symbol" which can also be any one of the conceptsymbols in the text analysis dictionary or the reserved symbol "$,"

(c) a "specified direction" indicated by one of the three letters "A, B,E" (A for ahead, B for behind, and E for either),

(d) a "specified distance" indicated by an integer,

(e) a "specified decision symbol" which can be one of the conceptsymbols in the text analysis dictionary or the reserved symbol "%," and

(f) a Boolean "specified operator retention" variable.

The computer applies individual rules in their order of entry intomemory as follows:

(a) The computer scans the text equivalent until a diagnostic symbolmatches the specified operator symbol. This matching diagnostic symbolis marked as the "matching operator symbol."

(b) If a matching operator symbol is found, the computer marksdiagnostic symbols in the text equivalent as "matching target symbols"based on the specified target symbol, distance and direction as follows:

(i) If the specified target symbol is "$," the computer marks thematching operator symbol as a matching target symbol.

(ii) If the specified target symbol is not "$," the specified distanceand specified direction are used as follows:

(1) If the specified distance is zero, then the computer compares thematching operator entry with the specified target symbol. If a match isfound, the computer marks the matching operator symbol as a matchingtarget symbol.

(2) If the specified distance is less than zero and the specifieddirection is "A," then the computer marks, as matching target symbol,all diagnostic symbols matching the specified target symbol if thesesymbols are ahead of the matching operator symbol in the textequivalent.

(3) If the specified distance is less than zero and the specifieddirection is "B," then the computer marks, as matching target symbol,all diagnostic symbols matching the specified target symbol if thesesymbols are behind the matching operator symbol in the text equivalent.

(4) If the specified distance is less than zero and the specifieddirection is "E," then the computer marks, as matching target symbols,all diagnostic symbols in the text equivalent matching the specifiedtarget symbol.

(5) If the specified distance is greater than zero and the specifieddirection is "A," then the computer marks, as the matching targetsymbol, the diagnostic symbol immediately ahead of the matching operatorsymbol in the text equivalent if the following symbol matches thespecified target symbol and if the intervening diagnostic distance isless than the specified distance.

(6) if the specified distance is greater than zero and the specifieddirection is "B," then the computer marks, as the matching targetsymbol, the diagnostic symbol immediately behind the matching operatorsymbol in the text equivalent if the preceding symbol matches thespecified target symbol and if the intervening diagnostic distance isless than the specified distance.

(7) If the specified distance is greater than zero and the specifieddirection is "E," then the computer marks, as the matching targetsymbol, the diagnostic symbol just ahead of the matching operator symbolin the text equivalent if the following symbol matches the specifiedtarget symbol and if the intervening diagnostic distance is less thanthe specified distance. If this following symbol is not marked, then thecomputer marks the diagnostic symbol in the text equivalent just behindthe matching operator symbol as the matching target symbol if the priorsymbol matches the specified target symbol and if the interveningdiagnostic distance is less than the specified distance.

(c) Transformations are performed on the text equivalent based onmatching target symbols as follows:

(i) If the matching target symbol is "$," then:

(1) If the specified direction is "B," the computer inserts thespecified decision symbol and then the specified distance into the textequivalent just prior to the matching target symbol.

(2) If the specified direction is "A," the computer inserts thespecified distance and then the specified decision symbol into the textequivalent just after to the matching target symbol.

(ii) If the matching target symbol is not "$," then:

(1) If the specified decision symbol is "%," then the specified targetsymbol and its following diagnostic distance are both deleted from thetext equivalent,

(2) If the specified decision symbol is not "%," then the matchingtarget symbol is replaced by the specified decision symbol.

(iii) If the specified operator retention variable is FALSE, then thematching operator symbol and its following diagnostic distance are bothdeleted from the text equivalent.

These transformations are now illustrated using the text equivalent ofstep II-A13 and three sample text equivalent transformation rules.

(a) Rule 1:

Specified operator symbol=n

Specified target symbol=d

Specified direction=A

Specified distance=10

Specified decision symbol=%

Specified operator retention variable=FALSE

This rule has the function of removing, from the text equivalent,references to non-defense matters. Application of this rule to the textequivalent of step II-A13, repeated here,

* 1 s 16 b 12 n 4 d 42 yields * 1 s 16 b 12.

In applying rule 1, the computer marks the "n" in the text equivalent asthe matching operator symbol. Since the specified direction is "A," thecomputer examines the diagnostic symbol after the "n" and finds a matchwith the specified target symbol of "A." The intervening diagnosticdistance of 4 is also less than the specified distance of 10. Sincethese two criteria are both met, the computer marks the diagnosticsymbol "d" as the matching target symbol. Since the decision symbol is"%," the matching target symbol and its following diagnostic distance of42 are both deleted. Then, because the specified operator retentionvariable is FALSE, the computer also deletes the matching operatorsymbol "n" and its following diagnostic distance of 4.

(b) Rule 2:

Specified operator symbol=b

Specified target symbol=s

Specified direction=B

Specified distance=-1

Specified decision symbol=+

Specified operator retention variable=TRUE

Application of this rule to the previously transformed text equivalent

* 1 s 16 b 12 yields * 1+16 b 12.

In this transformation, the computer finds and marks the matchingoperator symbol "b" in the text equivalent. Since the specifieddirection is "B" with the meaning of either ahead or behind and sincethe specified distance is less than zero, the computer searches for alloccurrences of the target symbol anywhere in the text equivalent. Thecomputer finds the matching target symbol "s." Since this was not thereserved symbol "%," the computer replaces the matching target symbol"s" with the specified decision symbol "+." Since the specified operatorretention variable is TRUE, the matching operator symbol is retained inthe text equivalent. In this way, it has been possible to combine themeanings of "American defense" represented by the "b" and "spending"represented by the "s" to give the meaning of "American defensespending" represented by the "+."

(c) Rule 3:

Specified operator symbol=+

Specified target symbol=$

Specified direction=B

Specified distance=0

Specified decision symbol=A

Specified operator retention variable=TRUE

Application of this rule to the previously transformed text equivalent

* 1+16 b 12 yields * 1 A 0+16 b 12.

In this transformation, the computer finds the matching operator symbol"+." Since the target symbol is the reserved symbol "$" and since thespecified direction is "B," the computer marks the matching operatorsymbol as the matching target symbol and inserts the specified decisionsymbol "A" and the specified diagnostic distance of 0 just before thetarget entry.

Rule 3 has permitted the extraction of individual meanings from conceptsymbols embodying several different concepts. In the example presentedabove, the concept of "America" denoted by the concept symbol "A" couldbe written explicitly in the text equivalent since "America" was alreadya part of the concept of "American defense spending" denoted by theconcept symbol "+." In the dictionary of Table 2, the word "Marine"alone was interpreted to embody the meaning of "American defense."Therefore, the concept of "America" was always implied although notexplicitly stated as a separate word.

Even though Marine can refer to other concepts besides a subset of theAmerican military. It is possible to let Marine have this connotation,if the set of collected AP dispatches almost always uses this word inthis context. Therefore, ambiguous words can have a precise meaning iftext containing other meanings is eliminated.

As a general rule, the text transformation rules lead to a series oftransformations resulting in a final text equivalent containing conceptsymbols representing directly the concepts used for the decisions for ablock of text.

(II-A16) After performing all the transformations specified by the textequivalent transformation rules, the computer makes a list of matchingcarryover symbols comprising of all concept symbols which are in boththe list of specified carryover symbols (entered at step II-A3) and inthe final text equivalent.

As an example, consider symbol "A" being in the list of carryoversymbols. In this case, the computer would enter this symbol in the listof matching carryover symbols if the text equivalent is the final one inthe previous step, namely,

* 1 A 0+16 b 12.

The use of "A" as a specified carryover symbol permits the connotationof "American" to be transferred from one block of text to the next. Thisdesignation is desirable when it is not certain that the next block oftext would refer to "America" unless the previous block of text had thisimplication.

(II-A17) The computer checks the text equivalent for any diagnosticsymbols which match the list of retention symbols. If any are found, thecomputer sets the Boolean block retention variable as TRUE. Otherwisethe block retention variable is set as FALSE. The computer then checksthe text equivalent for any diagnostic symbols matching the list ofdiscard symbols. If any matches are found, the block retention flag isset as FALSE.

The computer writes the block of text, including the <> marks and theirincluded concept symbols to the full text output file on disk.Immediately following this text, the computer writes the text equivalentboth before any transformations have been made and also after eachtransformation step. If the block retention flag is TRUE, the computerthen writes the words "PREVIOUS TEXT IS RETAINED." If the blockretention flag is FALSE, the computer writes "PREVIOUS TEXT ISDISCARDED" instead.

In addition, if the block retention flag is TRUE, the computer alsowrites to the filtered text output file the same items described abovefor the full text output file with the omission of the text equivalentsafter the transformations. Also, the phrases about the previous textbeing retained or discarded are also not written since only retainedtext is stored in the filtered text output file.

Continuing with the text of step I-3, it is possible to filter for theretention of only those paragraphs directly relevant to American defensespending. In this case, the "+" symbol could be used as the retentionsignal. It would be unnecessary to specify any concept symbols asdiscard symbols. In this case, the "+" in last text equivalent of stepII-A15 would cause the block retention variable to be set to TRUE. Theoutput to the full text output file for the block of text in step II-A15would be:

!"<s>Funding for the <b>Marines and <n>non-<d>defense items should notbe cut," he said.

* 1 s 16 b 12 n 4 d 42

* 1 s 16 b 12

* 1+16 b 12

* 1 A 0+16 b 12

PREVIOUS TEXT IS RETAINED

(II-A18) The computer repeats steps II-A7 to II-A17 above after findingthe next initiation signal in the text and moving the current beginblock of text marker to this initiation signal. The repetition stopswhen the end of the story is reached.

The second block of text from the edited text of step I-3 would be:

!He did ask for a reduction in health care <s>spending. with textequivalent:

* 42 s 9

The text equivalent transformation of step II-A14 would yield

* 42 s 9 A 0

after addition of the matching carryover symbol of "A" (identified instep II-A16) and the diagnostic distance of 0.

There is no change in this text equivalent following any of the Rules 1to 3 of step II-A15. Since there is no "+" symbol in the finaltransformed text equivalent, the computer sets the block retentionvariable to FALSE and writes the block of text, the original textequivalent, the three text equivalents after application of thetransformation rules, and the words "PREVIOUS TEXT IS DISCARDED" to thefull text output file.

Nothing from this second block of text is written to the filtered textoutput file. The result is a more homogeneous text containing only thoseparagraphs directly relevant to "American defense spending."

After steps II-A1 through this step, the filtered story of step I-3 is:

* 87.0027

!"<s>Funding for the <b>Marines and <n>non-<d>defense items should notbe cut," he said.

* 1 s 16 b 12 n 4 d 42

(II-A19) The text filtration steps II-A5 to II-A18 for individualstories are repeated for all retrieved stories.

(II-A20) If the text after step II-A18 retains important amounts ofirrelevant text, the computer loads a new dictionary and set ofcorresponding rules based on criteria different from those for stepsII-A1 to II-A4 and repeats the text filtration steps II-A5 to II-A18. Ifnecessary, this filtration to remove irrelevant text is repeated using anew alternative dictionary and corresponding set of rules each timeuntil reasonably homogeneous text is obtained.

The text used for these further filtrations is the filtered text outputfile of an earlier filtration. In loading the text in step II-A5, thecomputer removes all old diagnostic symbols by eliminating all characterstrings beginning with "<" and ending with ">." The computer alsoremoves the old text equivalent by removing the string of charactersbetween the "*" symbol marking the beginning of a text equivalent andthe carriage return marking the end of a text equivalent as shown instep II-A17.

II-B. Text Scoring

The stories are each scored for the extent to which they supportpositions relevant to public opinion change for the issue under study.

Continuing with the example from step II-A20 above, the task proceedsusing a text analysis dictionary, a set of transformation rules and aset of scoring rules all designed for the computation of numerical"message scores" favoring the three positions of More, Same and Lessdefense spending.

Every message is given an index number k. For this kth message, thescores are designated s_(ij"k) where index i refers to the source of theinformation as deduced from the message itself, where index j" denotesthe position the score favors, and where k is the message index. Index iis odd if the scored information directly supports a position so thatthe message is accessible to all members of the population. Index i iseven if the information indirectly supports the position so that onlythose already of the issue are able to make the connection. For exampleif the President of the United States is quoted in the kth message assaying that there should be More defense spending, then:

(a) i=an odd number (e.g. i=3) identifying the source as the President(the number is odd for defense spending because the message directlyadvocates a position on this issue; indirect Presidential assertionsrequiring interpretation by the audience would have another icharacteristic of the President but the index, e.g. i=4, would be aneven number),

(b) j"=1 if the position of More defense spending had this index, and

(c) k=the index number identifying the AP story from which the quotecame.

This definition for s_(ij"k) is the same as that in equation A.28 of Fan(1987). Because index i can have several values, reflecting differentmessage sources, it is possible for a message to have several scoreswith different indices i favoring the same position indexed by j".

In the simplest scoring for the defense spending example, nodistinctions would be made between messages according to source. In thisdegenerate case of no source dependence, i=1 for all sources, bothdirect and indirect. This is assumption made for the defense spendingscoring described below.

The actual scoring involves determining s_(ij"k) values for each blockof text and then summing the values for all blocks of text. Therefore,the additional term s_(ij"kq) is introduced where q is the index forindividual blocks of text. Thus s_(ij"kq) is the score for the ithsource, supporting the j"th position, of the qth block of text withinthe kth story. The final s_(ij"k) for a story is the sum of thes_(ij"kq) over all the q for that story.

The text scoring procedure itself is very similar to that for the textfiltrations and is performed on text remaining after all the filtrationsteps described in step II-A.

(II-B1) The text scoring uses a text analysis dictionary and a set oftext transformation rules with exactly the same formats as those insteps II-A. In addition, the task requires a set of "text scoringrules."

(II-B2) The computer reads and stores in memory the text analysisdictionary, the text transformation rules, and the text scoring rules.

Since the text scoring rules include concept symbols from the textanalysis dictionary, it is necessary to discuss the dictionary beforeconsidering the text scoring rules. Therefore, consider the example ofthe text from step II-A20. For this example, the dictionary couldinclude the fragments of the concept and dictionary arrays shown inTable 3 (the complete dictionary would have more entries):

                  TABLE 3                                                         ______________________________________                                        Concept and dictionary arrays of text analysis                                dictionary                                                                    ______________________________________                                        Fragment of concept array                                                     Concept mnemonic                                                                              Concept symbol                                                ______________________________________                                        AheadNegation   /                                                             LessWord        L                                                             SameWord        s                                                             MoreWord        M                                                             ______________________________________                                        Fragment of dictionary array                                                  Dictionary word Concept symbol                                                ______________________________________                                        not             /                                                             cut             L                                                             ______________________________________                                    

In this dictionary, the words with the concept of AheadNegation wouldcause a reversal in the sense of words further into the text equivalent.A typical example would be the word "not." LessWord would be a wordfavoring less spending. The example in the dictionary is the word "cut."Since the text filtration illustrated in step II-A above had alreadyrequired that the paragraph be about "American defense spending," thetext scoring step might simply be based on word clusters implyingsupport for More, Same or Less without regard to reference to America,defense or spending. In this case, the concepts of "less," "same," and"more" (LessWord, SameWord, MoreWord in the dictionary of Table 3) wouldbe enough for the text scoring rules.

These rules are a two dimensional array with elements S_(ij") where iindexes the sources of thoughts in the collected AP stories and j"indexes the positions favored by the message scores. Each elementS_(ij") has two components:

(a) a "scoring mnemonic" comprised of a strings of characters,sufficiently long to suggest the source of a thought and the positionthat thought is scored to support, and

(b) a corresponding "scoring symbol" comprised of a concept symbol. Theappearance of a scoring symbol in the qth text equivalent of the kthstory would lead to a positive numerical score s_(ij"kq).

Consider the task where all messages are scored independently of sourceso that i=1 for all scores. Furthermore, consider that MoreWord in Table3 implies support of More defense spending and corresponds to indexj"=1. Similarly, SameWord might imply support for Same spending withindex j"=2, and LessWord might imply support for Less spending withindex j"=3. Then, the scoring rules would have the form of Table 4.

                  TABLE 4                                                         ______________________________________                                        Text scoring rules                                                            The prefix letter "s" is frequently used to indicate a                        scoring mnemonic so that sMore, sSame, and sLess correspond to                scores supporting More, Same and Less defense spending. The                   scoring symbols are from Table 3.                                             S.sub.ij"k                                                                              Scoring mnemonic                                                                            Scoring symbol                                        ______________________________________                                        S.sub.11  sMore         M                                                     S.sub.12  sSame         S                                                     S.sub.13  sLess         L                                                     ______________________________________                                    

(II-B3) The computer loads the filtered texts of the collected storiesafter the last filtration of step II-A20. As for the filtration of stepII-A20, the computer removes from the text all strings beginning with"<" and ending with ">" and omits the text equivalents. The computerthen performs steps II-A5 to II-A16 using the text analysis dictionaryand the text transformation rules of step II-B2.

If the initiation and termination signals for blocks of text areparagraphs as in step II-A3, and if the dictionary is that in Table 4,then the computer would construct this text and text equivalent from thetext of step II-A20:

!"Funding for the Marines and non-defense items should </>not be<L>cut," he said.

* 54/8 L 14

Since the decision in the example of step II-B2 was simply to score wordcombinations favoring More, Same and Less, the following text equivalenttransformation rule could be used with "not less" being considered to beequivalent to "same:"

Rule 4:

Specified operator symbol=/

Specified target symbol=L

Specified direction=A

Specified distance=20

Specified decision symbol=S

Specified operator retention variable=FALSE

Computer application of this rule to the previous text equivalent

* 54/8 L 14 yields * 54 S 14.

(II-B4) After constructing the summary text equivalents by performingthe text equivalent transformations for the qth block of text of the kthmessage using the text transformation rules of step II-B4 above, thecomputer then uses the text scoring rules to calculate the s_(ij"kq)values in the following manner.

The computer compares the scoring symbols in the S_(ij") of the textscoring rules with the concept symbols in the summary text equivalent ofthe qth block of text of the kth message. The computer calculates thes_(ij"kq) for a particular i and j" by dividing the number of matchesfor the concept symbols in the corresponding S_(ij") by the total numberof all matches for all concept symbols in all the S_(ij") regardless ofi and j". By performing this division, the same total score is given forevery AP paragraph. If matches are found for two concept symbolssupporting different positions, the total paragraph score would bedivided among the corresponding concepts.

Using the text scoring rules of Table 4 for the transformed textequivalent of step II-B3, the computer obtains:

s_(11k1) =0/1=0.0 AP paragraphs,

s_(12k1) =1/1=1.0 AP paragraphs, and

s_(13k1) =0/1=0.0 AP paragraphs.

Here, the corresponding block of text is the first in the story so q=1.The units for s_(ij"kq) are the types of blocks of text scored. Sincethe scores above are for a typical AP paragraph, the units are APparagraphs. For calculating these s_(ij"kq) scores, scoring symbol "S"in Table 4 appears once while scoring symbols "L" and "M" appear zerotimes in the text equivalent. With only one appearance of a scoringsymbol, the total number of matches is 1+0+0=1.

To permit the system operator to follow the progress of the analysis,the computer writes to the monitor a full text output file in a formatvery much like the full text output file of step II-A17. The onlydifference is that a line describing the block scores replaces thesentences announcing the retention or discarding of previous text. Theblock of text scores in the output (beginning with the word "SCORES:")includes the scoring mnemonic in the S_(ij") with the same i and j" asin the s_(ij"kq) (see Table 4):

!"Funding for the Marines and non-defense items should </>not be<L>cut," he said.

* 54/8 L 14

* 54 S 14

SCORES: sMore=0.0, sSame=1.0, sLess=0.0

(II-B5) The computer repeats steps II-B3 and II-B4, for all blocks oftext q in the kth story. Upon scoring the last block of text, computersums the individual s_(ij"kq) over all q to obtain the final s_(ij"k)score. For the kth story, the computer writes the decimal date, denotedt_(k), and these final scores s_(ij"k) to disk.

Since there was only one block of text left in the example of stepII-A20, the final message scores s_(1j"k) are the same as the s_(1j"k1)scores in step II-B4 above.

(II-B6) The computer repeats steps II-B3 to II-B5 for the remaining textof all the stories after the filtration of step II-A20 and writes theirs_(ij"k) and their corresponding t_(k) to disk.

The examples presented above were chosen to illustrate the major optionsin the text analysis dictionary, in the transformation rules, in thetext filtration, and in the text scoring rules. The actual dictionaries,transformation rules, text filtration rules, and text scoring rules usedfor the analysis of American defense spending (Fan, 1987) were somewhatdifferent. Fan (1987) also describes the application of the textanalysis in this step II to five other topics. In all cases, the textanalysis was found to give acceptable results (see Chapter 3 and of Fan,1987).

III. Computations of Public Opinion

(III-1) The computer reads from disk all the data calculated in stepII-B6 and stores these data as a "scores array" with index k so thateach array element contains the t_(k) and all the corresponding s_(ij"k)of the kth story. The computer then sorts the elements of the scoresarray by date with the data from earliest story being the first item inthe array and the information from the latest message being the last. Inall subsequent steps, index k refers to the array index after sorting.

For example, if the story retrieved in step I-3 and scored in step II-B6had index k, then its entry in the scores array would be:

t_(k) =87.0027,

s_(11k) =0.0 AP paragraphs,

s_(12k) =1.0 AP paragraphs, and

s_(13k) =0.0 AP paragraphs.

(III-2) In order to compare predicted and measured public opinion, thesystem operator enters into the computer via the keyboard a seriesresults from measured public opinion polls. This series will typicallyinclude data from published opinion polls in which the same question orclose variants are asked at a number of different times. Since peopleare only allowed to hold one position for any single polled issue,opinion polls assume that the total population can be divided intosubpopulations, P_(j), each with members favoring the same polledposition with index j. The fraction of the total population insubpopulation P_(j) is defined as B_(j). The percentage in the NoOpinion or Don't Know category can be considered to be a separatesubpopulation or the percentage for this group can be removed with theremaining percentages for defined positions being renormalized to 100%.This nomenclature follows that in Sections A.2 and A.4 of Fan (1987).

Since public opinion can change, B_(j) will vary with time t so thatB_(j) =B_(j) (t). To indicate that opinion percentages are from polldata, the B_(j) (t) from polls carry the extra subscript P and thereforehave the form B_(Pj) (t). Different polls in a series are identified byindex numbers n so the nth poll result favoring position j is B_(Pnj)(t). If t_(Pn) is the time at which the nth poll was taken, withsubscript P again indicating that time is for a poll, then B_(Pnj)(t)=B_(Pnj) (t_(Pn)). The time t_(Pn) of a poll is computed by averagingthe beginning and ending date of the poll.

A series of poll data appropriate for the example in step I, is given inTable B.1 of Fan (1987). For the calculations in Fan (1987), thepercentage of Not sures and Don't knows were subtracted with theremaining 90% or so of the population favoring More, Same and Lessdefense spending being renormalized to 100%.

(III-3) Similar to the case with the message scores, the computer storesthe poll data as an array indexed by poll number n with each "opinionarray" element having the t_(Pn) of the poll and the correspondingB_(Pnj) (t_(Pn)).

The computer sorts the array elements according to date t_(Pn) with thepoll at the earliest date being the first element in the array. Insubsequent discussions, n will be the poll index after sorting.

For example, the first element of the poll array for the first line ofthe data of Table B.1 of Fan (1987) (after removal of the Not sure's andrenormalization as mentioned above) would have n=1. The correspondingelement in the poll array consists of:

t_(P1) =(31+28+15)/365=77.2027 (The date before conversion to a decimaldate was March, 1977. When only the month is supplied in the publishedpoll, the poll date is assigned to the middle of the month, hence March15, in the present example. The 31 and 28 are the number of days inJanuary and February respectively.),

B_(P11) =25.7%,

B_(P12) =49.5%, and

B_(P13) =24.9%.

(III-4) To predict public opinion, the computer loads a set of "refiningweight" constants w_(ij'j") prescribing the method for generating"persuasive force functions" G"_(j') describing the ability ofinformation favoring a position to change the minds of persons holdingdifferent opinions. Equation A.29 of Fan (1987) relating these items toeach other is reproduced below

    G".sub.j' (t)=.sub.i,j",k w.sub.ij'j".s.sub.ij"k.e.sup.-p(t-t.sbsp.k.sup.).sbsp..   (A. 29)

where the summation is over all i and j" and over all k with t_(k) <t.These indices are the ones entered for s_(ij"k) and t_(k) in step III-1above. Constant p is the "persistence constant" characteristic of APstories. Constants w_(ij'j") describe the contribution of each of thescores s_(ij"k) to persuasive force function G"_(j') (t). Eachpersuasive force function G"_(j') (t) favors a position denoted by j'.These positions often coincide with the positions j" of scores s_(ij"k)but need not do so as discussed in of Fan (1987).

These w_(ij'j") permit different types of information favoring aposition to have different weights. For instance, it is conceivable thatinformation from the President of the United States might be more orless persuasive than messages from Congress for the issue of whethermore should be spent for military defense. To take this possibility intoaccount, scores s_(3j"k) with index i=3 could refer to the quoted sourcebeing the President, and scores s_(5j"k) with i=5 could refer to thequoted source being from Congress. Both indices would be odd if thescores were due to direct quotes favoring the position indexed by j".Indices i=4,6 could be used if descriptions of Presidential andCongressional action indirectly supported a position on defensespending. A score attributed to the President (s_(31k)) and oneidentified with Congress (s_(51k)) could both favor the same positionsuch as more defense spending (j"=1), and both scores could come fromthe same kth story. These scores would be differentiated by theirindices i. If Presidential statements had greater persuasive force, thenw_(3j'j") would be greater than w_(5j'j").

In the simplest case, the persuasive forces would favor the samepositions as the scores contributing to these forces. Then, w_(ij'j")would only have a positive, non-zero value when j"=j'. However, thew_(ij'j") can also reflect ambiguities in the message scoring. Forexample, it is conceivable that the average message scored as favoringSame defense spending actually has a portion favoring More spending aswell. In this case, the s_(ij"k) favoring Same defense spending couldcontribute both to G"₁ (t) for More defense spending and to G"₂ (t) forSame defense spending with different weights w_(ij'j") for the twocontributions. If a position score s_(ij"k) makes no contribution topersuasive force function G"_(') (t), the corresponding refining weightw_(ij'j"), is zero.

The scores of step III-1 were for three positions j"=1,2,3 correspondingto support of More, Same and Less defense spending with no attributionby source so that i=1 for all scores. It is also reasonable to postulatethat the G"_(j') functions causing opinion change for the issue ofdefense spending only received contributions from scores favoring thesesame three positions. In this case, the refining weights could be givenby Table 5 where each s_(ij"k) only contributed to the persuasive forcefunction G"_(j") favoring the same position indexed by j".

                  TABLE 5                                                         ______________________________________                                        Refining weights                                                              In this Table, the non-zero w.sub.ij'j"  are indicated by their               appropriate index numbers.                                                                Index i for source = 1                                                        Index j" for scores s.sub.ij"k                                    Index j' for                                                                              favoring                                                          persuasive force                                                                          More        Same     Less                                         G".sub.j' (t) favoring                                                                    (j" = 1)    (j" = 3) (j" = 3)                                     ______________________________________                                        More (j' = 1)                                                                             w.sub.111   0.0      0.0                                          Same (j' = 2)                                                                             0.0         w.sub.122                                                                              0.0                                          Less (j' = 3)                                                                             0.0         0.0      w.sub.133                                    ______________________________________                                    

All values w₁₁₁, w₁₂₂, and w₁₃₃ would further have the same value if allscores s_(ij"k) were equally persuasive. Both the simple model of Table5 and equality in the w_(ij"k) values functioned well for the defensespending analysis of Fan (1987, Chapter 4).

(III-5) For opinion predictions, the computer also loads a set of"population conversion rules." These rules are summarized as an array ofconstants k'_(2j'rj). These constants are used in equation A.26 of Fan(1987). This equation is reproduced here: ##EQU5## The G"_(j') (t) termsare from equation A.29 (see above), R=the number of random messagescollected in step I, T=the total number of messages identified asrelevant in step I, Δt=the time interval used by the computer foriterative opinion calculations, B_(j) (t)=the percentages of thepopulation in subpopulations P_(j) as discussed in step III-2, and"modified persuasibility constants" k'_(2j'rj) (from Table 5) describethe ability of persuasive forces G_(j') to move persons from a "targetsubpopulation" P_(r) to a "destination subpopulation" P_(j).

The number of people persuaded to change their opinions from that ofP_(r) to that of P_(j) is proportional to the size B_(r) of the targetsubpopulation and the magnitude of the persuasive force G"_(j'). Theconstant of proportionality is the modified persuasibility constantk'_(2j'rj). If a G"_(j), cannot cause any conversion of people in P_(r)to join P_(j') then k'_(2j'rj) =0. For example, information favoringLess defense spending should not persuade those favoring Less spendingto support More spending. The computer assumes that all k'_(2j'rj) havethe same constant value denoted by k'₂ whenever a transition can becaused.

In FIG. 4.2 of Fan (1987) it is assumed that the population conversionsfor defense spending are as follows:

G"₁ converts members from P₃ favoring Less to P₂ favoring Same,

G"₁ converts members from P₂ favoring Same to P₁ favoring More,

G"₂ converts members from P₃ favoring Less to P₂ favoring Same,

G"₂ converts members from P₁ favoring More to P₂ favoring Same,

G"₃ converts members from P₁ favoring More to P₂ favoring Same,

G"₃ converts members from P₂ favoring Same to P₃ favoring Same.

These conversions would lead to the population conversion rules of Table6:

                  TABLE 6                                                         ______________________________________                                        Population conversion rules                                                   All non-zero k'.sub.2j'rj are entered with their appropriate                  indices and have the constant value of k'.sub.2. In this Table:               the position of More corresponds to j = 1, j' = 1, and r = 1;                 the position of Same corresponds to j = 2, j' = 2, and r = 2; and             the position of Less corresponds to j = 3; j' = 3, and r = 3.                 Index j' for                                                                           Index r for  Index j for destination                                 persuasive                                                                             target       subpopulation P.sub.j                                   force G".sub.j'                                                                        subpopulation P.sub.r                                                                      j = 1     j = 2 j = 3                                   ______________________________________                                                 r = 1        0.0       0.0   0.0                                     j' = 1   r = 2        k'.sub.2121                                                                             0.0   0.0                                              r = 3        0.0       k'.sub.2132                                                                         0.0                                              r = 1        0.0       k'.sub.2212                                                                         0.0                                     j' = 2   r = 2        0.0       0.0   0.0                                              r = 3        0.0       k'.sub.2232                                                                         0.0                                              r = 1        0.0       d'.sub.2312                                                                         0.0                                     j' = 3   r = 2        0.0       0.0   k'.sub.2323                                      r = 3        0.0       0.0   0.0                                     ______________________________________                                    

(III-6) Using the loaded rules, the computer performs calculations ofpublic opinion as a time trend using equations A.29 and A.26 as follows:

(a) The computer chooses as t, the time of the first poll t_(P1).

(b) The computer calculates, for time t=t_(P1) +Δt, all values ofG"_(j') (t) using equation A.29, the s_(ij"k) and t_(k) loaded in stepIII-1 from step II, constant p and constants w_(ij'j") assigned in stepIII-4.

(c) The computer uses the poll percentages, B_(P1j) at t_(P1) as thefirst B_(j) (t-Δt) in equation A.26. (The B_(r) (t-Δt) values are thesame.)

(d) The computer calculates B_(j) (t) at t=(t_(P1) +Δt) from these B_(j)(t-Δt), the R and T values calculated in the initial story retrievalstep I-2, the G"_(j') (t) calculated in step b immediately above, aspecified t, and the k'_(2j'rj) terms assigned in step III-5.

(e) The computer repeats the calculations of step b above afteradvancing time t by t to obtain values of G"_(j') (t) one t later. Thecomputer repeats step d using as B_(j) (t-Δt) the B_(j) (t) calculatedin the previous step d.

(f) The computer repeats step e, advancing time t in increments of Δt,until the t is greater than the t_(Pn) of the last poll time with indexn. The result is a set of values for B_(j) at times Δt apart. Thecomputer writes all these results to disk, and displays the opinion timetrend as a graph on the monitor and on the printer.

An example of these data plotted as time trends is shown in FIG. 4.6 Fan(1987). The measured B_(Pj) (t) values from the published pollsdescribed in step III-3 above are also plotted as squares forcomparison.

The computer also writes the calculated values of G"_(j) (t) to disk.The G"_(j) (t) functions used for the computation of FIG. 4.6 of Fan(1987) are plotted as the time trends of FIG. 4.4 of Fan (1987).

(g) During the calculations of step f, the computer calculates and sumsa series of "squared deviations" between the calculated opinion and thepublished opinion values. These squared deviations are calculatedwhenever the time t_(Pn) of a published opinion poll coincides with oneof the times t in the calculations of step h or is between two of thesetimes t.

If time t_(Pn) coincides with one of the times of step f, then thecomputer calculates, as the squared deviation, the square of thedifference between the calculated opinion B_(j) (t_(Pn)) and the pollvalue favoring the same position B_(Pnj) (t_(Pn)). A separate squareddeviation is calculated for each position j and all squared deviationsare summed.

If time t_(Pn) is between two calculation times t-Δt and t, and if Δt is24 hours or less, then the computer calculates the squared deviationsbetween the poll value B_(pnj) and both B_(j) (t-Δt) and B_(j) (t). Thesmaller of the two squared deviations is used for the summation. Thisdecision is based on the argument that there are at least 24 houruncertainties in the times of the polls and in the times of the APmessages so it is not unreasonable to choose the lesser of thedeviations for estimating the calculation errors.

If the Δt is longer than 24 hours, then the computer calculates theB_(j) (t_(Pn)) corresponding to the measured B_(Pnj) by performing alinear interpolation between the B_(j) (t-Δt) and B_(j) (t) to obtainthe B_(j) (t_(Pn)).

After calculations at all of the times of step g, the computer will havecalculated squared deviations for all poll points. The computer thencomputes a mean squared deviation by dividing the sum of the squareddeviations by the total number of deviations computed. The computerwrites the mean squared deviation to the display device and the printer.

(III-7) Optionally, the computer computes the mean squared deviation fora number of trial values of constant p, the k'_(2j'rj), and thew_(ij'j"). The computer chooses as optimal those constants those givingthe minimum mean squared deviation.

Alternative Embodiments

Although a specific example of the preferred embodiment is given above,a number of modifications of this embodiment are possible within thescope of this invention:

I. Alternative Messages and Scoring

Broadly speaking, the determinations in the preferred embodiment occurin three defined steps: collecting messages, scoring the messages, andusing the message scores to determine time trends of public opinion.Since these steps are largely independent, it is possible to vary boththe messages and their scoring.

The essential feature of the messages is that they must berepresentative of those available to the population and relevant to theissue for which opinion is computed. As discussed in Chapter 1 of Fan(1987), the messages can be from any source ranging from personalexperiences to those in the mass media. Any of these messages can beused in the computations of this patent so long as three criticalfeatures can be assigned:

(a) numerical scores for the extent to which different attributedsources in the message support different positions of the issue,

(b) a time dependent function describing the availability of the messageto the population, and

(c) a numerical validity score for the reputation of the mediumtransmitting the message.

Therefore, the following alternative methods can be used:

I-A. Alternative Messages

In addition to AP stories in the preferred embodiment, messages can becollected from any source relevant to the issue for which opinioncalculations are made. These can include other mass media messages bothin the written press and in the electronic press. The messages can be inany form ranging from personal experiences, through words and pictureson written pages to broadcasts via television and radio.

Besides actual messages which the computer can retrieve and score (seefollowing section), it is also possible to postulate specified numericalscores for items a and c in this alternative embodiment. Then amathematical function can be postulated for item b. With thesespecifications, it is possible to include in opinion calculationsmessages which cannot be measured easily but which can be modeledmathematically.

I-B. Message Scoring

Every message must be scored for the extent to which it supportsdifferent positions within the issue being analyzed. These scoress_(ij"k) in equation A.29 of the preferred embodiment were obtained bythe computer assisted content analysis of step II of the preferredembodiment.

This same procedure can be performed for any message comprised of textwhich can be transferred to characters readable by computer. If the textis only found as written words on paper, it is possible for the systemoperator to read and enter the text into the computer by use of thekeyboard. Alternatively, it is possible to use an electronic device toread the text and convert it into computer readable form.

I-C. Message Availability to the Population

For AP stories in the preferred embodiment, it was assumed thatinformation in an AP dispatch would have its maximum persuasive force onthe date of the story. After that time, the effectiveness was postulatedto decrease exponentially with time with a characteristic persistenceconstant. This time course is described in the e^(-p)(t-t.sbsp.k.sup.)term of equation A.29. This same time course can also be used for othermass media messages from newspapers, television and radio.

However, other mathematical functions may be more appropriate for othermessage sources. For example, information from a book may have a timecourse which increases over a substantial time period before finallydecreasing.

In principle, it is possible to postulate any arbitrary time dependentmathematical function to describe the availability of a message to thepopulation. Such a function would replace the e^(-p)(t-t.sbsp.k.sup.) inequation A.29. It is also possible to measure this availabilitydirectly. For a book, for instance, it is possible to approximate theavailability by the measured pattern of sales over time. Since thee^(-p)(t-t.sbsp.k.sup.) term is specific for each message with index k,this term could be replaced by a different measured or postulatedfunction for each message.

The discussion so far has only been for the time course of the messageavailability. More completely, the availability of the message alsoincludes a scaling factor describing the number of people reached at aparticular time--for instance when the message's persuasive force wasthe greatest. The larger the audience at this time, the greater will bethe total effect of the message.

If only AP messages are used, then this scaling factor will be constantfor all messages so the factor was absorbed into constant k'₂ ofequation A.26 (see Appendix A of Fan, 1987). This procedure means thatdifferent k'₂ should be used for messages from different media.Therefore, if messages from more than one medium are used, messages fromdifferent media will need different k'₂.

I-D. Validity of the Medium

Like the scaling factor for message availability just discussed, thereputation of the medium is also absorbed into constant k'₂ (see of Fan,1987) for the analysis of AP stories. Again, if other types of messagesare used, this k'₂ should differ according to the medium.

II. Replacements for G" Functions

In, it is proposed that the G" functions in equation A.26 can bereplaced by equation A.13 reproduced below:

    H.sub.jr (t)=G.sub.j (t)/(d.sub.jr.G.sub.r (t)+d.sub.jj.G.sub.j (t)+1) (A.13)

where d_(jr) and d_(jj) are both constants. In the preferred embodiment,these constants were assumed to be sufficiently small that their productwith the G functions in equation A.13 are much less than 1. Functions G"and G are related to each other by a constant factor as discussed in ofFan (1987).

To use this equation in opinion calculations, equation A.26 is replacedby equation A. 15 of Fan (1987) reproduced below:

    dB.sub.j (t)/dt=                                           (A.15) ##EQU6## This equation can be solved at intervals of Δt essentially as described in step III of the preferred embodiment once the H.sub.j'r (t) are calculated. All other terms in the equation have already been described in that step III.

III. Inclusion of Unawareness

So far in this patent, the assumption has been made that all people wereaware of the issue being analyzed. When a significant fraction of thepopulation is unaware, it is possible to use equations A.34-A.36 of Fan(1987) to determine expected public opinion (see of Fan, 1987 forjustifications). These equations are reproduced below: ##EQU7## for allj and j' where u is constant and ##EQU8## for all odd i, all j", and allk where t<t_(k). Finally, ##EQU9## for all j' and r.

The computer calculates functions F'_(j') (t) using equation A.35, andthe same scores s_(ij"k), w_(ij'j") values and constant p as appear inequation A.29. However, the summation this time is only over odd iinstead of all i. The computer then reads values for constants u and thek'_(lj'j) as well an initial value, typically from measured opinionpolls, for the fraction (1-A(t-t) of the population at (t- t) who areunaware. Employing these data, the computer can calculate A(t) atincreasing intervals of t using the A(t) of one calculation as theA(t-t) of the next calculation (see analogous strategy for calculationsB_(j) (t) as described in step III-9 of the preferred embodiment). Thecalculated values of A(t) and the data discussed above can then beinserted into equation A.36 to compute the fraction A(t).B_(j) (t) ofthe population holding the opinion with the corresponding index j.

IV. Alternate Concept and Diagnostic Symbols

In the preferred embodiment, the concept and diagnostic symbols allcomprised of a single character. Alternatively, it is possible to useconcept and diagnostic symbols containing any combination of data bits.For example, strings of characters can be used. It is only necessarythat reserved codes (not necessarily the "<" and ">" of the preferredembodiment) be used to mark the beginnings and ends of concept symbols.With these control codes, the computer can identify the beginnings andends of the concept and diagnostic symbols. With this identification,the computer can remove concept symbols and text equivalents from aprevious text filtration step before any further text analysis steps.

In the text of the output files from the preferred embodiment, thecontrol codes for the beginnings and ends of the concept symbols were"<" and ">". In the text equivalents, on the other hand, the controlcodes marking the beginnings and ends of the concept symbols werespaces. This difference illustrates that it is unnecessary that thecontrol codes be the same in the text and in the text equivalents in theoutput files.

V. Parsing of Text by Words Instead of Characters

In the preferred embodiment, words were defined as any arbitrary stringof characters. With this definition, comparisons between words in thetext and words in the dictionary were performed by permitting a word tostart at any character in the text. Also, any leading and trailingletters were permitted. Alternatively, it is possible to require thatthe words begin and/or end with defined control codes such as spaces,carriage returns, etc. In this case, the dictionary searches could befor words beginning or ending with a control character. The dictionarycould also have reserved control characters at the beginnings, in themiddles, and/or at the ends of word entries to indicate that replacementcharacters are possible for the control characters.

VI. Opinion Determinations without Reference to Poll Measurements

Opinion determinations in the preferred embodiment began with the firstB_(j) (t-Δt) being taken from the results of a public opinion poll.However, it is also possible to take advantage of the results of FIG.4.10 of Fan (1987). This figure shows that the opinion calculations willconverge to a consensus value as time proceeds regardless of the firstB_(j) (t-Δt). Therefore, it is possible to perform two opinioncalculations beginning with widely disparate values for an opinion B_(j)(t-Δt) e.g. 0% and 100%. Opinion in the time period during which the twocalculations converged could then be taken as a reasonable estimate ofexpected opinion.

VII. Alternative Determinations of Scores for Blocks of Text

In step II-B4 of the preferred embodiment, the block of text scoress_(ij"kq) for a particular i and j" were obtained after division by thesum of all s_(ij"kq). Alternative divisors with appropriate weightscould be used based on some combination of s_(ij"kq) scores. As yetanother alternative, there might only be the summing of the counts ofconcept symbols with no subsequent division step.

VIII. Alternate Use of the Specified Distance

When the specified distance is greater than zero in step II-A15 of thepreferred embodiment, a matching target symbol can only marked if it isseparated from the matching operator symbol by no more than onediagnostic distance number. Alternatively, the separation can involvemore than one diagnostic distance if the total of all interveningdiagnostic distances between the matching operator and target symbols isless than the specified distance. The same specified direction ruleswould still be used as in the preferred embodiment.

IX. Alternations in the Contents and Text Equivalents of Blocks of Text

In step II-A15 of the preferred embodiment, the essential concepts of ablock of text are summarized as a final transformed text equivalent. Theconcepts from one block of text can be transferred to the textequivalent of the following block of text by the use of carryoversymbols which are added to the following text equivalents (see stepsII-A14 and II-A16). This is done when the contents of one block of textimplied the presence of certain concepts in other blocks of text fromthe same story.

This procedure can be generalized in two ways. The first generalizationis to alter not just the text equivalent of the following block of textbut also text equivalents of other specified blocks of text. Themodification can involve not only the addition but also the deletion orreplacement of elements in these other text equivalents.

The other generalization is the alteration of the text of other blocksof text. For example, the sample text equivalent of step II-A16, carriedthe connotation of "American." Besides using carryover symbols to insertthe concept symbol for "American" into the text equivalent of the nextblock of text, it is possible to insert the word "American" into thetext of another specified block of text.

Words would be inserted in, altered in or deleted from the text of ablock of text when it is desirable that the words should continue toappear in all subsequent filtration or scoring steps for a block oftext. Conversely, if it is preferable that the connotation shoulddisappear in subsequent manipulations, it would be more appropriate tochange text equivalents since text equivalents are always erased beforeany following text analysis steps.

X. Hardware Based System

In this alternative embodiment, the microprocessor 10 and software(FIG. 1) is replaced by an alternative hardwired logic system.

Referring now to FIGS. 2 and 3, a block diagram and flow diagram,respectively, of the opinion prediction operation of this hardwaresystem is shown. The system includes text retrieval logic 50, textfiltration logic 52, numerical scoring logic 53, prediction and outputlogic 54, and control logic 56. Output logic 54 is connected to drive amonitor and a printer, while control logic 56 includes a keyboard input.Text retrieval logic 50 is connected to a communication link 51 fromwhich data may be acquired, for example, from a data bank. Digitalstorage unit 57, either random access memory or magnetic medium based,is connected to all logic modules 50, 52, 53 and 54.

Control logic 56 includes hardwired logic for controlling the otherlogic modules to perform the functions outlined in the flow diagram ofFIG. 3. Referring to FIG. 3, the start of operation of the system isrepresented by block 60. In block 61, logic 56 causes retrieval logic 50to command an external data base via a communication link 51 to retrievetext and to store it in storage unit 57, in the same manner as describedabove with respect to step I of the software embodiment. In block 62,retrieval logic 50 receives the text of retrieved messages from the database via the link 51 and stores the text in storage unit 57. If a signalindicates that more text is to be retrieved in block 63, the retrievallogic 50 repeats blocks 61 and 62. When no more text is to be retrieved,the logic 50 will have finished the equivalent of step I of thepreferred embodiment.

At this point, control logic 56 causes filtration logic 52 to activatethe first dictionary and its corresponding set of text filtration rules(block 64), which are predetermined and stored in storage unit 57. Logic52 uses this dictionary and set of rules to remove text irrelevant tothe prediction in the same manner as described above with respect tostep II of the software embodiment. At the end of this text filtration,the filtered text is stored in storage unit 57 and logic 52 checks forthe presence of a subsequent dictionary and its corresponding set oftext filtration rules (block 65). If another pair is found, logic 52repeats the filtration process using the paired dictionary and set ofrules. After no more filtration dictionaries and rules are found, thelogic 52 signals control logic 56 to activate the scoring logic 53.Logic 53 operates pursuant to present text scoring dictionary and rulesstored in unit 57 (block 66) and assigns numerical scores for theremaining text, in the same manner as described above with respect tostep II of the software embodiment. After calculation of the scores, thescores are stored in storage unit 57.

Before or during the operation of the system, the operator has theoption of entering the results of data from measured public opinionpolls into storage unit 57 via the keyboard and logic 56 (block 67). Inblock 68, control logic 56 activates prediction logic 54 which usesspecified parameters, refining weights w_(ij'j") and populationconversion rules k'_(2j'rj) stored in unit 57 to compute time trends ofpublic opinion in the same manner as described above with respect tostep III of the software embodiment. The results are plotted as timetrends on the printer 12 with reference to FIG. 1. For comparison, theprocessor also plots the results of time trends from measured opinionpolls. Logic 54 may optionally also compute statistical comparisonsbased on the differences between predicted and measured opinion values,if such values are available.

XI. Predictions for Habits

For the purpose of clarity and to aid in the understanding of theinvention, all embodiments presented above have focused on the abilityof information to change public opinion. However, opinion is only oneexample of a social trait. As indicated above, this invention alsoencompasses other social traits including habits like smoking.

The mathematical model in equations 1-7 of the Background of theInvention section above has already been extended to cover habits in"Ideodynamic predictions for the evolution of habits" by David P. Fan inJournal of Mathematical Sociology, Vol. 11, pp. 265-281, 1985. However,since that model also used equations 1-7, that theory extension was alsoinoperable.

Therefore, an alternative embodiment could involve adding some of theconcepts in that former extension to the embodiments described above toyield a functional system for predicting the fraction of the populationwith certain habits.

By their very nature, habits are activities performed at frequentintervals. Therefore, members of the population can be assigned tovarious positions based on the frequency with which they repeat theactivities. In the smoking example, both smoking and non-smoking wouldbe defined as habits. If desired, it is also possible to divide thesmokers according to the frequency with which they smoke. Each positionas defined in the preferred embodiment would have both a correspondinghabit and a corresponding opinion. However, the fraction of thepopulation with a habit need not match the fraction of the populationholding the opinion that the habit should be adopted. For example, theremay be many smokers who would like to quit.

In this embodiment for habits, opinion and habits are predictedindependently of each other. Nevertheless, the two predictions share thesame three steps of: (I) gathering messages relevant to the habit, (II)scoring the messages numerically for their ability to influencespecified target subpopulations, and (III) analyzing the ability of themessages, as represented by their scores, to change the habits ofappropriate target populations.

XI-A. Messages in the Mass Media

All messages relevant to opinion change for a habit are also be assumedto be pertinent to change in practice of that habit. Thus anti-smokingmessages would be assumed to able to influence both opinion on thedesirability of smoking and the chances that that people will stop.These messages would be those described in the preferred embodiment andwould lead to functions G"_(j') of equation A.29.

XI-B. Mathematical Functions Replacing Measured Messages

Some messages are difficult to measure directly. Since the ultimateimpact of messages is described by persuasive force functions G"_(j') inthe preferred embodiment, messages which cannot be measured cannevertheless be included in the analysis if they can be modeled bypostulated mathematical G"_(j') functions. For habits, the paper by Fancited earlier in this section includes two functions describing a"recidivism" effect and a "social pressure" effect. The recidivismeffect in turn incorporates functions describing the "nostalgia" and"euphoria" phenomena. These functions are described in detail below:

XI-B1. Recidivism Functions

A person who has recently changed a habit has a "nostalgia" for the oldhabit and hence a better chance of reverting to an old habit thansomeone who had never had the former habit. Again, in the smokingexample, a smoker who has quit will be more likely to start than someonewho has never smoked. The system of this invention can account for thisphenomenon by assigning mathematical "nostalgia" functions correspondingto personal experience infons favoring the start of smoking. Thesefunctions would have high values shortly after a change of habit withthe values diminishing as time proceeds.

Similarly, the system of this invention can also include "euphoria"functions describing the honeymoon feeling just after a change of habitin which a person is very happy to have successfully made the habitchange. As with the nostalgia function, the euphoria function will alsodecrease with time.

The euphoria and nostalgia functions were merged into equations 16describing the combined "net recidivism" effect in the paper by Fan inthe Journal of Mathematical Sociology mentioned earlier in this section.The combined effects yielded a recidivism persuasive force functionG_(jR) (t',t) with the form:

    G.sub.jR (t',t)=k.sub.R.e.sup.-k N.sup.(t-t').(1-e.sup.-k U.sup.(t-t')). (16)

This function uses the term G since it has the same purpose as the Gfunctions of step III of the preferred embodiment, namely the persuasionof susceptible members of the population to undergo social change. Inthis function, subscript j indicates the position toward which thefunction is likely to draw recruits and subscript R refers to thefunction describing the recidivism effect. The function is measured attime t and depends on an earlier time t' at which members of thesubpopulation left the habit characterized by subscript j. If G_(jR)(t',t) is the function describing the recidivism to smoking, then t'would be the time before the measurement time t when the smoker hadquit. Parameters k_(R), k_(N), and k_(U) are constants. In habitcomputations, this function G_(jR) is added to the G"_(j') functions ofequation A.29 and entered in place of the G"_(j') in equation A.26.

XI-B2. Social Pressure Functions

Besides the recidivism infon impact functions, it may also be convenientto postulate other functions G reflecting informational forces forsocial change. For example, in the Fan paper discussed earlier in thissection, it was postulated that there are social pressure messages whichcan be modeled by assuming that messages in favor of smoking ornon-smoking occur in proportion to the number of people observed inthese categories. This phenomenon is described by equation 9 of thepaper by Fan discussed earlier in this section:

    G.sub.jS (t)=k.sub.S.D.sub.j (t)                           (9)

where G_(jS) carries subscript S denoting that it is a social pressureinfon impact function. Subscript j refers to the position the functionfavors, k_(S) is a constant and D_(j) is the fraction of the populationpracticing the habit corresponding to the position indexed by j.

XI-C. Habit Trend Determinations

The basic system of this invention can be used to compute the fractionof the population with a habit by a method essentially the same as thatdescribed in step III of the preferred embodiment.

However, not all people would would like to change a habit may actuallydo so. Therefore, the k'_(2j'rj) terms in Table 6 describing the abilityof a message to cause a social change would be smaller for behaviorchange than for opinion change.

In the calculations, recidivism persuasive force functions, tailored foreach subpopulation, are added to the social pressure persuasive forcesand the G functions from measured messages as described above foropinion change. Since the recidivism persuasive force functions dependon the time of a prior habit change, the population needs to be dividedinto many small subpopulations depending on the time of the previoushabit change. The computations of step III of the preferred embodimentare made for the various subpopulations and the results for thesubpopulations are then summed to get the overall time trends.

XII. Predictions for Other Social Traits

For previous alternative embodiment extends the predictive methods ofthe system from opinions to habits. In principle, the methods can beextended further to any measurable social trait for which people can begrouped into subpopulations according to a specified set of positions.Such traits could include the purchase and use of products.

In all cases, the same methods would be used: The relevant populationwould be divided into appropriate subpopulations according to the traitsbeing measured and according to the sensitivities of the subpopulationmembers to various incoming messages. The members of a subpopulationwould all have the same trait such as not having purchased a product.The system would then calculate numerical scores for the ability ofvarious messages to convince people to make a measurable change such aspurchasing the product. The scores would then be used to computeexpected social change as a function of time. The result would be a timetrend for the social trait.

Thus it will be seen that this invention has a very broad scope givenits ability to predict social changes in all areas ranging from opinionsthrough habits to the purchase of products by consumers.

One important aspect of this invention is the use of computers todetermine the effectiveness of messages favoring different positions.This computer based technology permits an impartial and unbiasedassessment of the different components of messages supporting differentideas. Therefore, besides merely being used in the context of socialchange, the text analysis portion of this invention permits the user todetermine, for any purpose, the extent to which different ideas arefavored. For example, personnel management is of crucial importance inthe industrial sector. In order to hire and evaluate personnel, it isoften necessary to examine the texts of letters of reference and thecontents of text in personnel files. The text analysis portion of thisinvention will permit the employer to glean from such textualinformation as to whether an employee or prospective employee hasimportant qualifications or deficiencies in areas of particularimportance to given jobs. To be more specific, some jobs may requiregreat accuracy and patience while initiative and creativity might bemore critical for others. The text analysis of this invention willpermit the employer to scan and evaluate written reports about theemployee for comment on these traits.

The ability to predict the effect of information on social traits willalso be useful for industrial campaigns to persuade the work force toadopt desirable habits. An immediate example would be efforts to educateworkers about appropriate safety measures and to persuade them that themeasures should be taken seriously.

The coupling of text analysis with predictions of social traits permitsan examination of the amount and quantity of information needed to causea social change. Once of the unique features of this invention is itsability to include opposing information in analyzing the effects offavorable information. Thus, it is possible to evaluate the effects ofadvertising and public relations. A decrease in market share for acommercial product or favorable opinion for a position could be tracedto a competitor mounting an even more effective campaign than that ofthe original advertiser or public relations expert. The conclusion couldbe that nothing was done wrong but only that more needs to be done.

The following section of this patent is an excerpt from a manuscriptwritten by the inventor named in this patent. This section is referredto above as Fan (1987). The ending subsections of this section are forconvenience sake referred to in the main text of this section asAppendices A, B and C; these subsections are integral with this sectionand this patent, and not a separate part thereof.

PREDICTIONS OF PUBLIC OPINION FROM THE MASS MEDIA: Analysis by ComputerContent Analysis and Mathematical Modeling

by

David P. Fan

Department of Genetics and Cell Biology

University of Minnesota

St. Paul, Minn. 55108-1095

TABLE OF CONTENTS

Page

TABLE OF CONTENTS

1-a

DEDICATION

ACKNOWLEDGMENTS

2

INTRODUCTION

5

Outline of This Book

12

Organization of This Book

13

CHAPTER 1

FORMULATION OF IDEODYNAMICS

16

1.1 Strategies Used in Formulating Ideodynamics

16

1.2 Nature of the Population

18

1.3 Nature of Persuasion

19

1.4 Nature of Persuasive Messages

22

1.5 Relationships between Ideodynamic Structures

30

1.6 Overview of Opinion Calculations

31

1.7 Details of Opinion Calculations for the Awares

33

1.8 Details of Opinion Calculations for the Unawares

43

1.9 Time Scale of Ideodynamic Analyses

44

Figure Legends 1.1-1.3

46

FIGS. 1.1-1.3

47

CHAPTER 2

IDEODYNAMICS AND PREVIOUS MODELS

50

2.1 Significant Features of Ideodynamics

50

2.2 Model Comparisons

60

CHAPTER 3

DATA FOR CALCULATING PUBLIC OPINION

69

3.1 Time Series of Opinion Polls

69

3.2 Relevant Persuasive Messages in the Associated Press

73

3.3 Retrievals from the Nexis Data Base

75

3.4 Summary of Data used for Opinion Projections

78

Figure Legend 3.1

80

FIG. 3.1

81

CHAPTER 4

COMPUTER TEXT ANALYSIS BY METHOD OF SUCCESSIVE FILTRATIONS

82

4.1 General Text Analysis Programs

82

4.2 Strategy for Content Analysis Using Successive Filtrations

4.3 Sketch of Filtration and Scoring Computer Program Runs

87

4.4 Text Analyses for Defense Spending

89

4.5 Text Analysis for Troops in Lebanon

92

4.6 Democratic Primary

95

4.7 Text Analysis for the Economic Climate

97

4.8 Text Analysis for Unemployment versus Inflation

98

4.9 Text Analysis for Contra Aid

98

4.10 Summary Features of Text Analysis by Successive Filtrations

100

4.11 Extensions of the Text Analysis Procedure

104

CHAPTER 5

PROJECTIONS OF PUBLIC OPINION

106

5.1 Opinion Predictions for Defense Spending

106

5.2 Opinion Predictions for Troops in Lebanon

117

5.3 Opinion Predictions for the Democratic primary

120

5.4 Opinion Predictions for the Economic Climate

123

5.5 Opinion Predictions for Unemployment versus Inflation

124

5.6 Opinion Predictions for Contra aid

125

5.7 Summary of Constant Used in Poll Projections

127

5.8 Summary of Statistics for Poll Projections

128

Table Legends 5.1-5.3

130

Tables 5.1-5.3

131

Figure Legends 5.1-5.45

146

FIGS. 5.1-5.45

146

CHAPTER 6

METHODOLOGICAL SIGNIFICANCE OF WORK

191

6.1 Validation of Ideodynamics

192

6.2 Data and Issues for Successful Ideodynamic Calculations

195

6.3 Positions for Which Persuasive Messages Are Scored

199

6.4 Computer Text Scoring

200

6.5 Ideodynamic Calculations of Opinion Time Trends

203

6.6 Insensitivity of Predictions to the Starting Opinion Values

211

6.7 Interpretations for All Ideodynamic Parameters

211

6.8 Significance of No Opinion Change

212

6.9 Analysis of Persuasive Messages Acting on Public Opinion

213

CHAPTER 7

SIGNIFICANCE OF WORK TO THEORIES OF OPINION FORMATION

215

7.1 Mass Media Messages and Opinion Leadership

215

7.2 Reinforcing Role of Persuasive Messages

218

7.3 Cumulative Effects of Information Rather than Minimal Effects of theMedia

220

7.4 Caveats for Laboratory Experiments

225

7.5 Law of the 25-Hour Day

225

7.6 Interpretations of Ideodynamic Parameters

226

7.7 Nature of Effective Persuasive Messages in the Mass Media

229

7.8 Cause and Effect

234

APPENDIX A

MATHEMATICS of IDEODYNAMICS

239

A.1 Structure of Ideas

239

A.2 Structure of the Population

239

A.3 Structure of Messages

240

A.4 Nomenclature Simplification

244

A.5 Infon Properties

244

A.6 Infon Persuasive Force

245

A.7 Information Influencing the Unawares

245

A.8 Information Influencing the Awares

246

A.9 Effect of Information on the Population

249

A.10 Modifications for AP Infons Assuming No Unawares

253

A.11 Comparison with Uniform Distribution

259

A.12 Modification for AP infons Assuming Non-negligible Unawares

259

A.13 Extensions to Very Long Times

261

A.14 Models with No Dependence on Subpopulations

261

APPENDIX B

DATA FOR CALCULATING OPINION CHANGE

263

B.1 Defense Spending 1977-1984

263

B.2 Troops in Lebanon 1983-1984

265

B.3 Democratic Primary 1983-1984

265

B.4 Economic Climate 1980-1984

266

B.5 Unemployment versus Inflation 1977-1980

267

B.6 Contra Aid 1983-1986

267

Table Legends B.1-B.6

269

Tables B.1-B.6

273

APPENDIX C

SUMMARIES OF TEXT ANALYSES

279

C.1 Strategy for Content Analysis by Successive Filtrations

279

C.2 Text Analysis for Defense Spending--Including Detailed Example

279

1. Filtration to select for dispatches on American defense spending 279

2. Filtration to select for paragraphs on defense spending

281

3. Numerical scoring for three positions on defense spending

284

4. Numerical scoring for two positions on defense spending

288

5. Text analysis for defense waste and fraud

289

a. Filtration to remove dispatches not on American defense spending 289

b. Filtration to select paragraphs on defense waste and fraud

290

c. Numerical scoring for stories on defense waste and fraud

290

C.3 Text Analysis for Troops in Lebanon

290

1. Filtration to select for paragraphs on American Troops in Lebanon

290

2. Filtration to remove paragraphs on military action and Christmasentertainment

290

3. Numerical scoring for dispatches on troops in Lebanon

291

C.4 Text Analysis for the Democratic Primary

291

1. Analysis using bandwagon words

291

a. Filtration for paragraphs about candidates

291

b. Scoring using bandwagon words

291

2. Analysis using name count

291

C.5 Text Analysis for the Economic Climate

292

1. Filtration to eliminate dispatches on non-American economies

295

2. Filtration to select paragraphs discussing the economy

292

3. Numerical scoring

292

C.6 Text Analysis for Unemployment versus Inflation

292

1. Filtration to eliminate dispatches on non-American economies

292

2. Numerical scoring

292

C.7 Text Analysis for Contra Aid

293

1. Filtration to select paragraphs on Contra aid

293

2. Numerical scoring by Fan

293

3. Numerical scoring by Simone French, Peter Miene and Janet Swim

293

Tables Legends C.1-C.7

Tables C.1-C.7

APPENDIX D

DETAILS OF ACTUAL PUBLIC OPINION PROJECTIONS

304

D.1 Computations of Persuasive Forces

304

D.2 Population Conversion Models

304

D.3 Opinion Projections

305

REFERENCES

307

AUTHOR INDEX

317

SUBJECT INDEX

324

INTRODUCTION

This book concerns the power of information on society. The centralthesis is that public opinion can be swayed in a predictable fashion bymessages acting on the populace. When the bulk of the relevant messagesare in the press, then the press becomes the principal determinant ofsociety's attitudes and beliefs. Although previous work has suggestedthat the press is able to set the agenda, this is book is unusual indemonstrating that the press also is able to mold opinion within agendaitems.

The importance of the press on opinion has long been recognized. This isseen in the concept of governmental press censorship which was inventedlong ago. However, the assignment of the preeminent role of the press inopinion formation in a free democracy is in apparent conflict with asizable body of literature describing the "minimal effects of themedia." With this shield, journalists and editors could work withoutfeeling that every one of their daily choices was affecting opinion.

However, the conflict between press importance and its minimal effect ismore apparent than real. As summarized in Chapter 7, the impact of apiece of news is most appropriately assessed quantitatively. In otherwords, messages in the mass media should be given numerical strengths.Although any one news story or restricted group of media messages, canhave effects ranging from very small through very large, opinion canfrequently be computed from the cumulative effect of all news stories,most of which can indeed have relatively minimal effects. Therefore, ingeneral, the concept of the "cumulative effects ofinformation"--comprising mainly of mass media information for manyissues--is more useful than the law of minimal effects.

This idea of the cumulative impact of information still permits workingmembers of the press to proceed without constantly worrying about theeffects of their every word because individual news items are stilllikely to have small impacts. However, over the long term, all theeffects accumulate with the totality of press messages capable of beingthe major influence on opinion. Thus society should realize thatindividual messages can indeed have minimal effects with long termtrends being of great importance.

As just noted, this book does not propose that the press will always bethe dominant force in opinion formation. Rather, the hypothesis is thatit is the totality of relevant information which will shape opinion.Therefore, the press will only be the primary influence if othermessages are Of minor importance.

Obviously, the importance of the press is related to its credibility,This trust has no direct relationship to whether the public ranks thepress as credible in opinion polls. It is only essential that the publicas a whole use no alternate sources of information for polled issuesdiscussed in the mass media. For example, the press in closed societiesis not likely to be the main determinant of attitudes if its reputationis so low that sizable portions of the populace rely on rumor and theunderground press.

In an open society like that in the United States, press trustworthinessis likely to be greater. It was to explore the domain of media dominancein opinion formation that studies in this book were performed on avariety of topics. Issues were chosen from both the domestic and foreignpolicy arenas. The two issues with the clearest foreign policyimplications concerned whether more troops should be sent to Lebanon(1983-1984) and whether American aid should be sent to the Contra rebelsin Nicaragua (1983-1986). The domestic topics included those ongovernmental policy and economic issues. The policy question was whethermore should be spent for national defense (1977-1986). The two economicissues focused on whether unemployment or inflation was the moreimportant problem (1977-1980), and whether the economic climate wasimproving (1981-1984). The remaining domestic issue was voter preferencefor the best candidate in the Democratic presidential primary(1983-1984). For all these cases, the mass media has the principal rolein influencing public opinion.

For the Democratic primary, the press could not be expected to be thedominant influence if there were important additional informationalsources including campaign advertising. It was to avoid the complicationof such non-mass media information that the Democratic primary wasstudied before the Iowa caucuses, a time when national media storiesshould have been the most important for opinion nationwide. At theseearly times, campaign advertising was negligible countrywide whileopinion poll results were obtained from this large population base.

From the discussion above, a quantitative analysis is able to reconcilethe minimal effects of the media with the cumulative effects ofinformation. Obviously, such quantitative assessments imply amathematical analysis, and, indeed, this book describes the newmathematical model of ideodynamics for calculating the impact ofinformation on the population. This model was constructed on the premisethat time trends of opinion percentages could be predicted from therelevant messages available to the public.

This model also has the important feature that it can unify manyseemingly conflicting results. A useful analogy is to the story of blindmen reporting on an elephant. The man studying the leg could report thatthe elephant was like a tree trunk while the man examining the tailcould find that the elephant was most like a rope. The contradictionvanishes when the entire elephant is considered in overview with boththe leg and tail being special cases of the more general model which isthe elephant. In the same way, the cumulative effects of information canencompass both individual groups of mass media messages having minimaleffects and the totality of the media having major effects.

The unifying power of ideodynamics derives importantly from itsquantitative nature. By giving numerical values to the contributions ofdifferent phenomena, there is no need to assert or imply that certainphenomena are always more or less important than others. Instead, thequestion becomes the relative importance of different phenomena underspecific circumstances. For instance, this book also demonstrates thatopinion formation is frequently affected rather little by reinforcementof previous opinion due to resolution of cognitive dissonance in thedirection of favorable information. This statement does not deny theexistence of opinion reinforcement and does not assert that suchreinforcement is never important. In fact, such reinforcement isexplicitly included in ideodynamics. Instead, the statement is merelythat such reinforcement is small relative to the forces in the massmedia causing opinion change for cases like the six studied in thisbook.

The elephant analogy can be extended to the emphasis in this book on theglobal behavior of the population. The concern is less with the behaviorof subpopulations and selected media messages than on the effect of thetotality of messages on attitudes within the entire population. In theanalogy, the theory is less concerned with the behavior of the parts ofthe elephant during locomotion than on the path of the elephant as awhole. There is no implication that the elephant's path is moreimportant to study than the effects of the legs, for example, onelephant movement. The analogy is only pursued to highlight the factthat this book is mainly about the macro effects of the totality ofinformation on overall attitudes without a systematic dissection of allcontributing factors, even though some such dissections are performed.

The theory is also formulated with very few parameters so that it can betested empirically. The empirical testability means that confidence inthe model could be derived from the finding that stories in theAssociated Press could give good time trend predictions of publicopinion percentages over time spans ranging from 3 months to 9 years.The success of applications to real data is crucially important becauseit could demonstrate that the approximations and calculated populationparameters are reasonable, even though some might seem heroic at firstglance.

Among the parameters examined, the most interesting lead to theconclusions that there is no lag before the onset of persuasion, andthat the impact of a mass media message decreases exponentially with ahalf-life of only one day. This means that the effect is entirelydissipated within a week. These results argue that there is no two steptransfer of information from the press to the populace via opinionleaders. Rather, the people are influenced directly by the mass media.Examination of the equations also show why the big lie can be effectivein propaganda, why the cause of fringe groups can be helped byterrorism, and why the political left and right can both accuse thepress of unfair bias.

To be consistent with the previous discussion on the importance ofquantitative assessments, these parameters might have other values infuture studies resulting in different implications for othercircumstances

The mathematical predictability of opinion indicates a large publicmalleability in the hands of the mass media. This malleability is likelyto arise from the law of the 24-hour day which is first introduced inChapter 1. This law simply acknowledges that the public is constantlybombarded by new information with so much being available that a personcan only reflect carefully on a small fraction. As a result, mostinformation is taken at face value.

This importance of superficial information is at the very heart of thewords reputation and prejudice. These words both imply decision makingbased on observations or information from the past. By bringing suchprior information to bear, an individual is spared the time and effortneeded to make a careful detailed examination of the current details ofthe issue. In fact, the time needed to make careful evaluations of allcurrent information simply may not be available.

These considerations stress another of the recurring themes in thisbook, the importance of real time for examinations of social issues. Itis not enough to describe pathways and sequences for social changeswithout an appreciation of the time spent in each step. For example, ithas already been mentioned that the constraints of real time lead tosuperficiality in decision making for the population as a whole. Suchsuperficiality is not likely to be observed when people are forced toponder issues carefully in laboratory studies, focus groups andinterviews where people are asked to reconstruct their states of mind.

An important consequence of the superficiality forced by time limitationis the ability to use mathematical equations to calculate public opinionfrom persuasive messages. For a wide variety of issues, like thosementioned above, persuasion is furthermore due to information largelyconfined to the mass media.

Throughout this book, the emphasis has been on message impact withlittle discussion of message generation. This emphasis certainly doesnot mean that message senders work in a vacuum, oblivious to otherfactors including actual or anticipated public opinion. In fact, bothChapters 2 and 7 discuss how message generation can be dependent onopinion within the context of the model. However, the interdependence ofopinion formation and message generation do not exclude these twophenomena from being studied separately.

In the same way, even though a nuclear war can involve an exchange ofmissiles, it is still possible to study separately missile damage andmissile launch. Indeed, a thorough understanding of missile impact willaid in an complete analysis of nuclear war. By analogy, a completedescription of the persuasive process can benefit from a careful studyof the impact of communications on the populace. An accurate descriptionof message effect can then be used as a firm base from which to continuethe analysis of message generation.

The key to uncoupling missile launch from missile impact is a validdescription of the pertinent properties of the missile, namely, itstrajectory and megatonnage. Once these properties are recognized, it ispossible to model both missile launch and impact in terms of theseparameters. Given the appropriate parameters, the analyst of missileimpact can predict the devastation without regard for the factorsinfluencing the launch.

In the same manner, once persuasive messages are coded in terms of theequivalents of the trajectory and megatonnage, knowledge of the sendersmotives are not important in considering message effect. An importantgoal of this book was to develop and validate parameters which aresufficient to describe a persuasive message without regard for themessage sender. A later analysis could then turn to message generation,with the messages coded in the same terms. When both message generationand impact are understood, then the trade of messages in a persuasiveprocess can be explored in the same way that an exchange of missiles canbe examined for a nuclear war.

The work in this book focused on message impact rather than messagegeneration because impact was likely to be more predictable. The law ofthe 24-hour day argues that opinion will usually reflect messages. Incontrast, opinion is more frequently only one factor rather than thesole factor in influencing the message sender. In addition to opinion,new discoveries and facts can greatly affect the messages broadcast. Ifnot, nothing new would ever be disclosed by the mass media since thevery novelty of a discovery must mean that very few people are aware andhence that there is very little opinion favoring the dissemination ofthis rare event. Therefore, a thorough analysis of the dissemination ofmass media messages must include not only an examination of opinion butnew events which are unpredictable by their very nature.

The foreseeable response of the populace to information is clearlyimportant for understanding social behavior for issues as trivial asfads and as profound as war and peace. For instance, the predictabilityand consequent superficiality of information absorption suggest that theaverage member of the public in modern democracies may make no morecarefully reasoned decisions for most issues than persons in moreprimitive societies. Furthermore, since the model should apply to allsocieties, the predictions should be as valid in dictatorships and as indemocracies so long as all the information available to the public canbe coded.

Outline of This Book

As discussed above, this book explores the new mathematical model ofideodynamics describing social responses to information. Although theoutlines have already been published, the model has been modified as aconsequence of its application to empirical data, the focus of thisbook. Therefore, this book begins with a presentation of ideodynamicsfollowed by an examination of the ability of the model to incorporateprevious theories (Chapters 1 and 2). Then the book considers dataapplications (Chapters 3 to 5). At the end (Chapters 6 and 7), there isa discussion of the conclusions to the be drawn from the work.

The empirical testing of the model involves its use to predict publicopinion from information in the mass media. The computed opinion is inthe form of percentage support of polled positions with the valuesappearing as continuous time trends calculated every 6 or 24 hours.Therefore, to the extent that polls are like snap shots, the trends fromthese new computational methods are like moving pictures, capable offilling in the gaps between actual poll points and extending opinionestimates to times when polls have not yet been taken.

The studies in this book show that mass media messages--as exemplifiedby Associated Press stories--can be used to predict the opinionpercentages published by reputable national polling organizations suchas ABC News, one of the major poll sources for this book. Successfulprojections were made for all six of the issues analyzed. For eachissue, the procedure consisted of:

(1) gathering the texts of AP dispatches relevant to the issue,

(2) scoring each story for the extent to which it supported differentpositions within the issue,

(3) using these scores in the equations of ideodynamics to computeopinion time trends, and

(4) comparing the computed time trends with published poll data.

Each study was based on two new methods. One was a previously unreportedcomputer procedure for scoring AP stories. The other involved computersolutions for the equations of ideodynamics.

Organization of This Book

Chapter 1 describes the deduction of ideodynamics from known phenomenain the area of persuasion. An appreciation of this chapter is essentialfor understanding the opinion computations.

Ideodynamics considers persuasive messages to have structures similar tothat of MIRVed missiles. The analogs of the independent warheads of theMultiple Independent Reentry Vehicles are message components each oneable to have an impact on appropriate target subpopulations. Thesemessage components are called infons. For example, a persuasive messagerelevant to defense spending could have one infon or component favoringmore spending, another infon favoring same spending, and yet anotherfavoring less spending. Like MIRVed missiles, all the infons are bundledtogether in the same persuasive message and launched at the population.This chapter models the effects of infons on the populationmathematically.

Chapter 2 discusses the main features of ideodynamics in the context ofprevious models for the impact of information on society, especiallythose in the area of public opinion. Therefore, readers primarilyinterested in the new methodology can skip this chapter.

Chapter 3 describes the data used for the calculations and thereforeshould be read.

Chapter 4 describes the new text analytic method used for obtaininginfon scores for the messages obtained for Chapter 3. Readers lessinterested in computer content analysis than opinion projections neednot read Chapter 3.

This chapter is free standing, describing a general technique of contentanalysis able to score any text for the extent to which different ideasare favored. The methodology is not restricted to generating scores forinfon support of different positions. For example, it is also possibleto use this text analysis for other purposes such as assessing whether aletter recommendation comments favorably on specific traits for personbeing discussed.

Since the major function of Chapter 4 is to produce infon scores, it ispossible to bypass this chapter, for opinion projection studies, by useof alternate scoring procedures. The most straightforward would be toask human judges to score the persuasive messages.

However, the computer methods do have distinct advantages: The criticalfeatures of the persuasive text are explicitly defined. Large amounts oftext can be scored. And, all scoring criteria are applied uniformly tothe entire body of text examined.

Chapter 5 describes how infon scores for persuasive messages are used tocompute expected public opinion and is at the heart of this book fromthe standpoint of opinion projections.

Each of the six studies in this book is considered in detail. Most ofthe final results fall into four major categories: (1) a set of graphsdescribing the time trends of persuasive information favoring differentpositions, (2) a set of graphs comparing published opinion poll resultswith opinion calculated on the basis of infon scores and the first setof published opinion percentages, (3) a set of graphs showing theoptimizations of the various constants in the ideodynamic equations, and(4) a table showing the goodness of fit based on the squares of thedifferences between the poll projections and the opinion percentages inpublished polls.

Chapter 6 examines the implications of the results of the studies onfurther applications of the method. Strictly speaking, this chapter neednot is be read by those interested only in the technical aspects of themethodology. However, this chapter is even useful for methodologicalconsiderations since it examines both the strengths and robustness ofthe techniques as well as the weaknesses and limitations.

Chapter 7 discusses the broader significance of the work in this book totheories of effective persuasion and examines the procedures by whichideodynamics can be extended to include theories of message generation.

Appendices. This book is written so that the reader can follow the mainthrust of the arguments without a detailed study of either themathematics of ideodynamics or the computer text analyses. However, bothof these technical areas are explained more fully in the appendices(Appendix A for the mathematics of ideodynamics, Appendix D for thecomputer calculations of opinion based on ideodynamics, and Appendix Cfor the computer text analyses). The primary data for the analyses arealso presented (Appendix B). References are made to the appendicesthroughout the text.

Further technical details of the procedures and computer programs usedfor this book are given in a pending patent application.

CHAPTER 1 Formulation of Ideodynamics

The main thesis of this book is that information controls publicopinion. For many issues in a free democracy, the driving force foropinion change is persuasive messages in the mass media. Support forthis thesis derives from the ability to use messages in the press tocalculate time trends of public opinion. The calculations are performedby computer and are divided into two main areas, those for text analysisand those for assessing the impact of information on opinion.

The studies are Grounded in a general model for information impactapplicable to the adoption of both behaviors and attitudes. In thisbook, however, the discussion will focus on attitudes since theapplications are restricted to public opinion. Appendix A presents animproved version of the mathematical model which has already been calledideodynamics (Fan, 1984, 1985a and 1985b). The name ideodynamics isdrawn from ideo to refer to ideas and dynamics to emphasize changes withtime.

In order to present the arguments without undue distractions, thecurrent chapter discusses the formulation of ideodynamics with minimumreference to alternative models. Relationships to other models arediscussed in Chapter 2.

Ideodynamics was developed to explain a number of known featuresconcerning the formation of public opinion. Therefore, the model isdeduced from phenomena which needed to be explained and shares thedeductive approach used by other workers like Downs (1957) in AnEconomic Theory of Democracy.

1.1 Strategies Used in Formulating Ideodynamics

One of the essential considerations in formulating ideodynamics was thatthe model should be testable using data from observations. Thiscondition is important since, as with any mathematical model,simplifying approximations are needed. The predictive powers of a modelprovide a good test of its soundness. If the approximations are validfor a large number of circumstances, the model should successfullypredict measured values for many cases. If the approximations areappropriate for only a small number examples, then the predictions fromthe model should frequently fail.

Therefore, empirical testability provides a method for assessing thevalidities of the approximations. As with any set of simplifications, itis always possible to imagine complications which will lead to failureof the approximations. Nevertheless, the model can succeed in a largenumber of instances if the complications usually make only minorcontributions within the total constellation of relevant phenomena. Theusefulness of the simplifications will depend on the extent to which theresulting predictions are accurate. To guard against the possibilitythat the accuracy is fortuitous, the model can be tried under a varietyof conditions. The calculations will gain in robustness as acceptablepredictions continue to be obtained. The important advantage ofempirical testability is that a crucial criterion for a model's successcan be the predictions obtained. In this way, the validity need not relysolely on the plausibility of the argument.

Testability, however, is clearly a two edged sword. Although accuratepredictions can argue that imagined concerns are of minor importance,consistently inaccurate predictions would also force abandonment of themodel.

Once a model can be tested empirically, it is both possible anddesirable to be bold in postulating simplifying approximations. Afterall, if the simplifications are too extreme, then the model will fail togive useful predictions. Therefore, a reasonable strategy is to beginwith the minimal model involving the smallest number of parameters. Morecomplicated approximations involving more parameters would only be addedif the minimal model did not give good predictions.

Another important advantage of using simple approximations is that themathematics and resulting computations are less complicated. Not onlywould the procedure be simpler to understand, but fewer errors would bemade in formulating the mathematical theory and performing the resultingcalculations.

With these considerations, ideodynamics was developed using quite simpleapproximations. For many public issues, the population was assumed tofollow blindly the information in the mass media. Interestingly, thissimple model did give reasonable opinion projections suggesting that themedia is not only responsible for setting the agenda (Cook, et al.,1983; Erbring, et al., 1980; Funkhouser, 1973a and 1973b; Funkhouser andMcCombs, 1972; Iyengar, et al., 1982; McCombs and Shaw, 1972; MacKuen,1981 and 1984) but is also the key agent in determining opinion.

1.2 Nature of the Population

Since one of the major requirements was empirical testability,ideodynamics was structured so that tests could be applied using readilyavailable data, namely those from public opinion polls.

The starting point for any opinion poll is a question relating to aparticular issue. In ideodynamics, the issues are defined as they are inopinion surveys. In particular, issues are topics for which members ofthe populace can each hold only one of two or more mutually exclusivepositions or ideas. For instance, the first issue in this book concernsAmerican public opinion on funding for military defense. This issue wasdefined as having three ideas favoring more, same, and less spendingsince these were the positions in several published polls.

Since public opinion polls divide people into subpopulations, eachholding a different viewpoint, ideodynamics also divides the populationinto subpopulations along the same lines. However, the model makes thedistinction between individuals unaware of the issue and persons awareof the topic and holding an opinion. The unawares comprise a portion ofthe No Opinion or Don't Know groups in opinion polls. The awares aresubdivided into those holding each of the permitted answers to polledquestions. The Don't Knows might also include some who are aware but areundecided. The defense spending analysis in this book ignored the NoOpinions including both the unawares and the awares but undecidedbecause the No Opinions were few in number, usually comprising less than10% of the total population. That left the three subpopulations ofawares supporting more, same or less spending. The differences intreatment between the awares and unawares are discussed in Appendix Aand Section 1.7 below.

1.3 Nature of Persuasion

Having defined issues, positions, and subpopulations as is done foropinion polls, it is possible to turn to the manner by whichideodynamics analyzes persuasion and calculates opinion percentages. Forthese analyses, ideodynamics notes that persuasion occurs in two steps:(1) message generation by senders, and (2) message impact uponreceivers. The vital links between these two steps are the messagesthemselves.

One of the key simplifying assertions of ideodynamics is that themessages, when properly coded, can include all the relevant informationabout the senders needed for the accurate assessement of message impact.Such coding simplifies the analysis by enabling separate analyses formessage impact and message generation. Therefore, it becomes possible tostudy messages and their effects without simultaneously considering howthe messages were created.

The Introduction has already drawn the analogy between persuasiveactions and intercontinental ballistic missiles. To assess the effectsof missiles, it is unnecessary to know where they came from so long ascertain key features such as trajectory and megatonnage are available.The effects will be as devasting regardless of whether the missiles weresent by accident or by design.

In the same way that missile effect can be determined without knowinganything about the missile sender--so long as the relevant traits of themissile are known, it should be possible to compute the effects ofpersuasive messages on receivers without knowing anything about thesource. It is only crucial that the pertinent traits of the message becoded.

Clearly, message broadcast and message impact are not independentevents. Some message senders are likely to be receivers and vice versa.Also, message sources are likely to change their messages afterinteraction with receivers. For example, Rogers (1983) stresses theimportance of message receivers asking senders for more information.

However, any interaction between message sender and receiver will stillproceed by way of messages. Therefore, it is still possible to separatethe analysis of persuasion into the two distinct portions of messagegeneration and message effect--so long as the messages themselves can becaptured and analyzed.

After separating message generation from message effect, it is still bepossible to study interactions between message senders and receivers.For example, if a receiver transmits a question to a sender, then thatquestion, itself, is a message. The person receiving the question canthen become a sender and send a message in response. To examine thebehavior of this person, it is sufficient to know that a message wasreceived in the form of a question. The impact is to cause the sendingof the answer, another message. Interactions between message generationand impact are discussed further in the final chapter of this book.

Having argued that proper coding obviates the need for furtherinformation about message senders, it is necessary to consider in detailthe message coding scheme used in ideodynamics. This coding must takeinto account the fact that different messages will have differenteffects on different subpopulations.

The differences in effects have been examined by many authors inconsidering cognitive dissonance and the "minimal effects" of the media.These two topics are related to each other, with communication theoristsdating back to Lazarsfeld, Berelson and Gaudet (1944) proposing that theprincipal effects of the media are not to convert opinion but toreinforce it. In other terms, people are likely to suppress dissonantmessages while preferentially selecting information favoring theirposition to reinforce their current viewpoint. This concept of the"minimal effects" of the media still has adherents (Chaffee, 1975;Klapper, 1960; Kraus and David, 1976; McGuire, 1986; Rogers, 1983)although this viewpoint is not uncontested (Graber, 1984;Noelle-Neumann, 1984; Page, Shapiro, and Dempsey, 1987; Wagner 1983).

Any model which can account for opinion reinforcement needs to accountfor an important logical consequence of the resolution of dissonance andthe resulting reinforcement of previous opinion: The population must bedivided into subpopulations holding different positions, just the typeof subdivision identified by polls and used in ideodynamics. That isbecause information favoring a position should only reinforce opinionamong people supporting that position. In the defense spending case, forexample, a message supporting more spending should only reinforceopinion in the subpopulation already favoring this idea.

It should further be noted that the concept of minimal effects does notmean "no effects" of the media. Thus the media is permitted to havesome--perhaps small--effect in changing minds within a population.Similarly, cognitive dissonance is not assumed to result invariably inreinforcement. On rare occasions, dissonance can be resolved in favor ofthe dissonant information. Consider again, the case of informationfavoring more defense spending. Although the major effect might be toreinforce those already supporting this position, it is possible thatthe same information might also have a weak conversion effect toincrease the numbers of people favoring this position. The peopleconverted must have held some other viewpoint earlier such as thatsupporting same spending.

Even though it may small relative to reinforcement, the conversioneffect can be very important. In fact, the essential question from thestandpoint of opinion is whether reinforcement is so strong that nochange occurs at all. In the absence change, opinion will stay staticand invariant, a situation which is known to be false for a large numberof issues. For any issues where opinions do change, reinforcement cannotbe so overwhelming as to block all shifts. If changes can occur, thecrucial element in determining public opinion is the residual amount ofpersuasive force, however, small, which can override the reinforcinginformation since those are the effects which will cause the opinionalterations.

The critical role of factors overriding reinforcement is reminiscent ofthe work of Granovetter (1973, 1978, 1980) on "thestrength-of-weak-ties." This author looked at the sources from whichpeople learned about the jobs they took. The finding was that the mostuseful sources were frequently those with which the job-seeker hadrelatively little interaction. Reinforcement in the present job did notleed to job changes so interactions leading to reinforcement wereunimportant regardless of their frequency or intensity. The key elementwas information about new jobs even if that information was rare. Ifthere was sufficient reinforcement for the original job so that a persondid not change employment, then that non-change would not have beenrecorded in the Granovetter studies. The finding that weak ties are veryimportant to changes in state are in agreement with ideodynamics whichargues that the mass media may have as its main function thereinforcement of a person's viewpoint, but projections of public opinionmust focus on an analysis of the few factors which do induce change.

1.4 Nature of Persuasive Messages

The previous section has discussed how the same message can affectdifferent subpopulations differently with most of those in favor of amessage's position being reinforced and a few of those opposed beingconverted. Messages in ideodynamics are coded to account for bothopinion reinforcement and conversion.

For simplicity, consider the issue of American aid to the Contra rebelsin Nicaragua (1983-1986). For this issue (see following chapters), therewere only the two positions of pro (favoring continued aid) and con(opposing continued aid). If a message favors only the pro position,that message should reinforce the opinion of the pros and might converta few of the cons. If a con message arrives at the same time, then thiscon message should reinforce the cons and might convert some of thepros. With these two messages, both the pro and con positions would bereinforced. Simultaneously, there could also be opinion conversions inboth directions with pro message recruiting the cons and con messageconverting the pros.

Logically, it is plausible that the result should be the same if the twomessages were not in separate communications but were part of a singlemixed message. Returning to the analogy of messages being ballisticmissiles, the model is that a single launched message could split into anumber of Multiple Independent Reentry Vehicles. A message with twocomponents favoring the pros and cons would be like a MIRVed rocket withtwo warheads, one favoring the pro position and the other the conposition. The assumption is that the target would be indifferent towhether the warheads were sent in one missile or in two separatemissiles. The end effect would be as devastating.

Therefore, the analysis can be made in terms of message subunitsfavoring different positions, each one being analogous to a singleIndependent Reentry Vehicle and being treatable as a persuasive entityin its own right. For ease of reference, the word infon was defined torefer to the concept of "a message component favoring one of thepossible positions being considered." The first part of this term isfrom the word information and refers of persuasive messages. The endingof infon is the same as that in elemental entities such as electrons andintrons in the physical and biological sciences. In fact, ideodynamicspostulates that infons are also elemental in that the entire persuasivepower of a message can be coded in the properties of its infons.

Infons are categorized in four dimensions:

(1) The first dimension is the position favored by the infon. Since eachinfon favors a specific position, an infon is only defined once thepositions under consideration have been specified. In fact, the samemessage can have infons defined differently for different issues. In amessage discussing both defense spending and Contra aid, for example,the infons for the defense spending analysis would be defined in termsof favoring more, same or less spending. The same message used in aContra aid study would be coded as having different infons, this timeeither favoring or opposing aid.

(2) The second dimension of the infon refers to whether the infondirectly or indirectly supports its position. This distinction is usefulbecause persons aware of the issue and its associated arguments can drawan inference from indirect data while an unaware individual can only bepersuaded about an issue if there is a direct statement in the messageabout the issue. For instance, someone unaware that defense and domesticprograms competed for the same funds could not connect a deficiency in adomestic program with defense spending while someone aware of theassociation might.

(3) The third dimension refers to the sender or source of the infon.Using this dimension, information favoring more defense spending fromtwo different sources--such as the President of the United States andCongress--could be assigned to two different infons. This distinction ismade in case some sources have more persuasive powers than others as hasbeen studied extensively by Page, Shapiro and Dempsey (1987).

(4) The fourth dimension gives the index number of the messagecontaining the infon. Therefore, all infons from the message labeled asmessage 1 would carry the index number of 1. As just discussed, theinfon would be further be identified by its position (the firstdimension), its directness (second dimension), and its source (the thirdindex).

In summary, although several infons within a message can favor the sameposition, each infon can only support one position. Whenever a messagesupports more than one position, that message must be divided intoinfons, at least one for each of the positions favored.

Every infon is identified by indices reflecting the four dimensions ofposition favored, directness, source and the index number of the messagecontaining the infon. An example would be an infon supporting moredefense spending (first dimension) resulting from a direct statement(second dimension) by the President of the United States (thirddimension) in a message indexed by the investigator as message number 1(four dimension). The purpose of specifying the four dimensions is topermit infons to be grouped for further analysis. All groupings arebased on these dimensions.

After a message is subdivided into its infons for the topic under study,each infon is then assigned three properties independent of each other.These properties are assumed to be sufficient to explain the infon'spersuasive effects in the same way that trajectory and megatonnage willyield a description of the damage from nuclear warheads:

(1) The content of an infon is a numerical score describing the abilityof the contents of the infon to persuade appropriate subpopulations. Inthis book, the measured infons all came from AP dispatches so infoncontent was measured as the number of typical AP paragraphs favoring theinfon's viewpoint.

(2) The validity of an infon is attributed to the reputation of themedium of the message. Like the infon content, the infon validity scoreis also numerical. Typically, a receiver is unable or does not have thetime to check the reliability of the medium so the receiver depends onits general reputation. For instance, the validity will be much higherfor a trusted friend than for a total stranger. Since this book's infonscame from AP dispatches, all validities were assigned a valuecharacteristic of the AP which was the medium.

(3) The audience size of an infon is a mathematical function describingthe numbers of people exposed to the infon as time proceeds. Since it isa mathematical function, the audience size is unlike the infon's contentand validity scores which are numbers. Obviously, the audience size iszero before the infon is emitted. Also, the audience size will be muchlarger for an infon from the mass media than one from a personalexperience where there is only one receiver. The audience size will havea very short duration for a one-on-one conversation returning to zero assoon as the discussion terminates. The equivalent duration for a bookcan be quite long since the book may continue to be sold and read formonths.

With this brief overview, the individual properties of infons can beconsidered in greater detail:

The infon content score combines aspects of both salience anddirectionality as used by previous authors for coding persuasivemessages. To compare infon content scores with schemes used by others,consider, for instance, two messages, A and B, both providing 100%support for the position of more defense spending. Suppose that messageA is more persuasive because more is said, because what is said is moreeffective, or because the quoted source is more credible. Theeffectiveness can be due to cognitive and/or affective appeals (Abelson,et al, 1982; Conover and Feldman, 1986; Marcus, 1986; Rosenberg, et al,1986).

One type of score used for persuasive messages has been directionality.For instance, Page and Shapiro (1983a) have coded a number of newsstories in the New York Times newspaper and in television evening newson a five point scale from "clearly pro," "probably pro," "uncertain orneutral," "probably con," to "clearly con." With this method, bothstories A and B favoring more defense spending would be given a score ofclearly pro.

Story A, being more persuasive, would either have a higher salience or agreater "quality" which has been defined by Page and Shapiro (1983a) toinclude the "logic, factuality, and degree of truth or falsehood."Therefore, messages A and B would be characterized by two differentquality and salience scores and a common directionality score.

The scoring in ideodynamics is somewhat different. The first step is todefine the positions relevant to the polled question. Any number ofpositions is permitted. Then, for each persuasive message, ideodynamicsassigns one or more infons to each position with different infons havingdifferent sources and directness of appeal. Each of these infons willthen have a characteristic content, validity and audience size. If themessage has no component supporting a particular position, then thecontent scores of the infons favoring that position are zero. Therefore,besides incorporating the trait of directionality, the content scorealso incorporates the salience and quality values because the higher thesalience and/or quality, the higher will be the content score.Obviously, salience is not only included in the content of the messagebut is also related to the message's audience size as will be discussedin the next section. The infon content score, therefore, incorporatesportions of the concepts of directionality, salience and quality.

Returning to the example of defense spending, it is possible to specifya very simple structure where infons are defined to favor only the twopositions of more spending or less spending. Also, it is possible toconsider only direct infons and to make no distinctions based on infonsource. In this case, infons will only be distinguished by the indexnumber of the corresponding message and the positions favored. When thissimple structure is applied to the two hypothetical messages A and B,both favoring only more defense spending, both messages would have acontent score of zero for the infon favoring less spending. Since bothmessages support more spending, the content scores of their infonsfavoring this position would both be positive with the more persuasivemessage (message A) having the higher score.

An important advantage of the ideodynamic coding of messages is thatinfons can easily code quite complicated issues where there are manypositions. This flexibility was demonstrated in the example of theDemocratic primary of 1983-1984 (see following chapters). For thisissue, persuasive messages were divided into six different infonsfavoring the positions of advantageous for John Glenn, advantageous forWalter Mondale, advantageous for Others, disadvantageous for Glenn,disadvantageous for Mondale, and disadvantageous for Others. It wouldobviously have been more difficult to use a single pro-con scale todistinguish six positions.

Since the content score describes the ability of the infon to persuadethe audience, this score depends on the interpretation by the messagereceivers rather than by the sender. Since social changes result fromchanges among the receivers, it is their perceptions which are ofgreatest importance.

The decision to code directionality of mass media messages from thereceivers' point of view was also taken by Page and Shapiro (1984)although these investigators have subsequently coded theirdirectionality in terms of the intent of the message source (Page andShapiro, 1987). The change was not made for theoretical reasons butrather for ease of scoring. However, these authors noted that there wasgenerally good agreement between the two approaches for informationscoring in the mass media. The coded material was either text from NewYork Times articles or summaries from the Television News Index andArchives from the Vanderbilt Television News Archive. It is conceivablethat conflicts between a sender's intention and a receiver's perceptionmight have be more pronounced if there had been inclusion of non-verbalmessages such as those transmitted via a television screen.

Validity was the second property assigned to infons and referred to thereputation of the medium carrying the message containing the infon.

Introduction of the validity score recognizes that the audience makestwo credibility decisions about information attributed to a quotedsource. First, the audience must decide that the quoted source actuallysaid what was reported. This decision is given in the validity scorereflecting the reputation of the medium. However, the audience must alsoevaluate whether the quoted source is trustworthy. This credibility isincluded in the infon content score.

Infon validity as used in ideodynamics has not always been includedexplicitly by other investigators in assessing the effects of the massmedia. For example, Page and Shapiro (1984, 1985, 1987) assume thatinformation in the New York Times and over network television both havea very high validity in that the public will assume that the Presidentactually made a statement if he is quoted as having done so. Allanalyses were performed entirely in terms of the credibility of thesources quoted by the medium. This high reputation of the medium isclearly reasonable for much of the mass media in the major Westerndemocracies.

However, in the more general case, the medium can contribute importantlyto the believability of information since sources like trusted friends,respected news wires and personal experiences will have high validitieswhile sources like suspected pathological liars and untrustworthyscandal sheets will have low validities.

The importance of the medium has been recognized for many years. Forinstance, in 1959, Hovland (1959) had already proposed that the impactof a message was greater in an experimental setting when the subjectthought the medium of message had the sanction of the investigator (seealso Eagly and Himmelfarb, 1978).

The third infon characteristic was audience size. This property issimply the curve describing the number of people exposed to the messagecontaining the infon as time proceeds. Messages like AP dispatches havelarge audience sizes just after emission with the audience decreasinggradually thereafter. For the mass media, it is convenient to introducefour more terms, the time at which the message is broadcast, apersistence constant describing the rate at which the message becomesinaccessible to the population, a memory constant describing the rate atwhich people forget about the infons in the message, and the audiencesize at the broadcast time. These four terms can describe the audiencesize at all times for AP messages (see Appendix A).

Like the content score, the audience size also incorporates aspects ofthe salience of persuasive messages. The ideodynamic audience sizeincreases when more people are exposed due to messages having highersalience.

1.5 Relationships Between Ideodynamic Structures

Infons are the last of the important structures within whichideodynamics organizes data. At this point, it is useful to consider therelationships between the basic ideodynamic structures discribingissues, messages and the population.

At the center is the issue which is divided into any number of mutuallyexclusive positions. The generality of the model is reflected in thelack of limitation on position number. Both the population and messagesare organized according to the positions of the issue.

The population is divided into subpopulations following members'responses to opinion polls. Each subpopulation favors a unique positionof the issue. As noted in Section 1.2 above and as will be discussedbelow, this definition of subpopulations permits an explicitmathematical modeling of the resolution of cognitive dissonance in favorof opinion reinforcement.

Section 1.4 described how all messages are divided into infons with eachinfon able to favor only one position. Although there is frequentlygreat overlap, the positions of infons need not coincide absolutely withthe positions corresponding to the subpopulations. For example, in theexample of the Democratic primary, there were infons disadvantagingMondale. There was no corresponding poll or subpopulation positioncorresponding to opposition to Mondale. On the other hand, it was alsopossible to perform the analysis (see following chapters) with infonsscored as favoring only more or less defense spending with no infonssupporting same spending. At the same time, there was a subpopulationmeasured as favoring same spending.

1.6 Overview of Opinion Calculations

Ideodynamics examines opinion formation using issues with mutuallyexclusive positions, and using subpopulations and infons each favoring asingle position. The strategy is to analyze opinion structure through anexamination of opinion change. The argument is that opinion can becalculated at any later time if opinion is available at an earlier timeand if all intervening opinion changes are known. In the same way, thelocation of an automobile is defined if an earlier position is specifiedand if the entire subsequent pathway is Given. This emphasis on analysisof change is also at the heart of epidemiologically based modelspredicting logistic increases for the adoption of innovations like newtechnologies (Bartholomew, 1976; Hamblin, et al., 1973).

The modeling through change permits public opinion to be dependent onopinion at an earlier time. The dependence on past opinion is oneimportant means for a population to reflect its history. If there wasmuch successful persuasion in the past so that opinion favoring aposition became high, then opinion would still be favorable if there wasvery little change in the meantime. In contrast, if this favorablepersuasion had not occured, then there might be few people holding thefavorable viewpoint at the earlier time. As a result, there would stillbe very few people in favor if there was not much intervening opinionchange.

In focusing on opinion alterations, ideodynamics assumes that infonsfavoring a position will cause members of appropriate subpopulationsholding different viewpoints to change their minds. The power of infonsto affect opinion are described mathematically using "persuasive forcefunctions" as described (see Appendix A).

One infon can alter the persuasive power of another due to phenomenalike opinion reinforcement and message saturation. Reinforcement meansthat an opposing infon would be weakened and be less able to causeopinion conversions. Therefore, ideodynamics models opinionreinforcement by permitting infons favoring a position to decrease thepersuasive force functions for opposing infons. Infons repeated tooshrilly and frequently can lose their effectiveness. Ideodynamics alsopermits this excessive propaganda to have diminishing returns.

Once the persuasive force functions have been formulated, it isnecessary to examine the expected effects of all the different infonsacting on the population. Considering again the analogy with ballisticmissiles, the infons of a message are like the component warheads of asingle MIRVed rocket in that all these infons are launched together.Then the individual infons would hit different target subpopulationswith different effects.

For example, infons favoring more defense spending are assumed inChapter 5 to convert members supporting the same spending position tofavor more spending. However, it is not necessary for an infon favoringa particular position to recruit persons only to that position. Again,in the defense spending case, infons favoring more spending were alsoassumed to be able to cause those favoring less spending to move halfway and support same spending. The specifications of the appropriatetarget subpopulations and the resulting conversions for all possiblesets of infons are given in "population conversion models." These modelswill vary from issue to issue.

Ideodynamics assumes that the larger a susceptible target subpopulation,the greater will be the conversion. This statement is equivalent tosaying that more deaths will result from a nuclear missile landing in adensely populated area. In addition, opinion conversion is increased ifthe persuasive force function is higher in the same way that more deathswill also occur if the megatonnage of the warhead increases. Thesearguments are both incorporated into the basic "ideodynamic equationsfor opinion change." Since change is proportional to both the size ofthe target subpopulation and the strength of the persuasive messages,the terms in the ideodynamic equations are non-linear and thereforediffer significantly from linear models such as many in econometrics.

1.7 Details of Opinion Calculations for the Awares

Following the principles just enunciated, the actual projection ofpublic opinion proceeds in three main steps:

(1) The first step is to construct mathematical functions describingpersuasive forces due to infons.

(2) The second step is to develop a "population conversion" model foreach issue studied. As mentioned above, infons favoring a position arepresumed to act on appropriate target subpopulations to convert aportion of the members to join other subgroups.

By studying ensuing changes, there is no need to be concerned with thereasons for the initial state reflecting the previous history of thepopulation. Therefore, any opinion poll can be taken as the startingtime for the opinion projections. From that time forward, an analysis ofintermediate attitudinal changes will be sufficient for calculatingopinion at any later time.

(3) The last step is to calculate public opinion using the infonpersuasive force functions and the ideodynamic equations for opinionchange corresponding to the population conversion models.

Each of these three steps is discussed in greater detail below focusingon opinion derived from AP stories since those were the examples in thisbook. This discussion begins by examining a population made up entirelyof awares holding different positions. The modifications for theunawares are considered in Section 1.8 below.

As noted in Section 1.6 above, the awares are able to digest indirectinformation as well as direct information so persuasive force functionsfor the awares include both classes of infons. Each infon's persuasivepower is encoded as a persuasive force function which changes with time(Appendix A, Equation A.28). As justified in Appendix A, the persuasiveforce function for an individual infon can be approximated by theproduct of the infon's content score, the validity score, and theaudience size function. That is, the persuasiveness of an infonincreases if there is a larger content score in favor of a position, ifthe medium has a higher reputation, and/or if the audience size isincreased.

While each infon has its own characteristic content score, most APinfons for any one issue will have approximately the same reputationduring the entire time periods studied so the same constant validityscore can be assigned to all AP infons.

Besides the content and validity scores, the persuasive force functionis also dependent on the audience size. Since this function varies withtime, it is the audience size function which will govern thetime-dependent shape of the persuasive force function.

So far as audience size is concerned, it is assumed that AP infons havetheir maximal effect on the day of their transmission with that effectdwindling exponentially with time. The exponential drop seems plausiblesince public exposure to information in print media is likely todecrease continuously, but rapidly, with no shard cut-off.

The rate of decrease is characterized by a "persistence" constant whichhas been optimized (Chapter 5) to have a one day half-life suitable forall issues studied. With a one day half-life, the persuasive effect of amessage drops to one-half after one day, one-quarter after two days,one-eighth after three days, and so on.

Although newspapers published AP stories a day or so after the dates ofthe dispatches, the dispatch dates themselves were used for thecalculations in this book since it seemed likely that radio andtelevision messages with the same approximate content would haveappeared on the dispatch dates.

As described in Chapter 4, AP messages were scored for the numbers ofparagraphs supporting each of the positions within the issue beinganalyzed. Since the scores were in numbers of paragraphs (Chapter 4),the heights of the persuasive force plots for AP infons are given inparagraphs. FIG. 1.1 shows the persuasive forces for two separate infons(top two frames) favoring the same position. These functions describethe strengths of the infons with respect to their ability to causeopinion change.

The simplest model for the combined effect of two infons would involveadding the persuasive force functions of the individual infons (FIG.1.1, bottom frame). This addition requires the assumption that the twoinfons behave as independent units. The justification for thisapproximation is based on the second law of thermodynamics which can berestated as the "law of 24-hour day."

Time does not go backwards so every individual, regardless of intellect,capability or interest, only has the time to consider thoughtfully avery small number of issues. Due to this time constraint, knowledge mustbe superficial for the vast majority of issues about which a person hasopinions. Therefore, only a very small percentage of the public will beexperts able to form carefully considered opinions for any given issue.Instead, each issue will have its own collection of experts with thoseexperts changing from issue to issue. Downs (1957) has also noted thatthe bulk of the population does not devote much time or effort tocareful analyses of most issues.

For instance, most people have neither the time nor resources toconsider seriously all the factors relevant to the question of whetherdefense spending should be increased or a decreased. Nevertheless, 90%or more of the population (Appendix B) usually had opinions on thistopic. Without the time or background to study the problem in detail andhence to relate different pieces of information with each other, themajority of the public is likely to treat all new infons as independentpieces of information. In fact, the population might even treatindividual phrases within persuasive messages as independent infons(Chapter 4).

The independence of persuasive force functions also bears on themodeling of opinion reinforcement. That is because the impact of infonsis described by these functions. Therefore, if reinforcement occurs, thepersuasive effects of opposing infons will be decreased resulting in apersuasive force function with a smaller value.

There are two basic methods for including reinforcement in the model.The most direct is simply to reinterpret a message as having aconversion infon with a lower persuasiveness if reinforcing informationis present. This decreased persuasiveness means a lower persuasive forcevalue at the measurement time and could arise from the content of theconversion infon having a lower content score or a smaller audience sizeas people avoid unfavorable information. The infon content scores inthis book were obtained by computer to assure consistency. This strategywould have been difficult to apply if the interpretation of all infonsdepended on the context of other persuasive information within both thesame and other messages received by the public.

However, it is also possible to model opinion reinforcementmathematically based on infons scored as if there were no other infonsaround. In such a procedure, small message units such as AP paragraphswould be scored for the positions they supported without regard toparagraphs in either the same AP story or in other stories. Then, theability of a reinforcing infon to diminish a conversion infon would bemodeled by mathematically decreasing the persuasive force function forthe conversion infon if a reinforcing infon had occurred earlier(Appendix A, Equations, A.12 and A.13). For these equations, an infonwhich is more effective at making opinion conversions also has a greaterability to reinforce.

This mathematical approach has the advantage that the correspondingequations contain constant parameters describing the importance ofreinforcement. Fortunately, satisfactory opinion calculations wereobtained when these parameters were set to zero. This result isconsistent with the law of the 24-hour day and the idea that most peoplefor most issues are sufficiently distracted that they do not spend thetime to associate one piece of information with another. As a result, itwas possible to ignore reinforcement in computations of expected publicopinion.

The phenomenon of opinion reinforcement is but one example of infonsinteracting with each other. Although a reinforcing infon cantheoretically decrease the activity of a conversion infon, it is alsopossible that continued rapid repetition of conversion infons can alsolead to information saturation with diminishing effects for additionalinfons. This possibility is included in Equation A.13 (Appendix A). Aswith reinforcement, message saturation could also be ignored with theircorresponding constant parameters being set to zero in empirical testsof the model.

Since additivity of infon persuasive force functions gives acceptableopinion calculations, it is possible to ignore all infon interactions,even those not associated with opinion reinforcement and informationsaturation. the result of additivity is that the combined persuasivepower of the two infons (FIG. 1.1, bottom frame) is the sum of theindividual effects (Appendix A). In other words, the residual effects ofthe first infon is added to the effects of the second.

This same strategy of ignoring message interaction was also adopted, forinstance, by Page and Shapiro (1987), who scored the directionality oftelevision news from the viewpoint of an "intelligent, attentiveaudience with average American beliefs and values." These investigatorsmade no effort to relate the contents of one message to another, eitherin the scoring or in the subsequent calculations.

Once the persuasive force curves are drawn, the next step is toconstruct a population conversion model giving the likely effects of thevarious persuasive forces on each of the subpopulations. For clarity, itmight be useful to consider the example of defense spending with thepopulation conversion model of FIG. 1.2. In this figure, the names forall the subpopulations begin with B (for "believers" in a viewpoint) andthe names for all persuasive force curves began with G in reference tothe G" functions used for the calculations (Appendix A, Equation A.29).Therefore, the subgroups were BMore, BSame and BLess and the persuasiveforce functions were GMore, GSame, and GLess favoring, respectively,more, same and less spending.

Each action leading to an opinion change is represented by an arrow. Thename(s) besides the arrows refer to individual persuasive forces. Thetail of the arrow leads from the target population whose size isdecreased by the persuasion. The arrow head points to the destinationpopulation whose number is increased in the same process. In FIG. 1.2,infons favoring more spending are assumed to be able to persuade thosefavoring less spending to alter their opinion to support same spending.The same infons can also act on those already favoring same spending tosupport more spending.

In this figure, there is a "sequential conversion" of those favoringless spending to favor first same spending and finally more spending.This model makes no comments about the length of time a person must stayin the same spending group en route from favoring less to more spending.The time can be almost instantaneous for some people and much longer forothers. The essence is that more information is needed to move from lessto more than from less to same.

Different infon persuasive force functions can act on the samesubpopulation. For instance, infons favoring more and same defensespending are both assumed to persuade those preferring same spending tofavor more spending and to convince those holding the less spendingposition to prefer same spending (FIG. 1.2). At the same time, a giveninfon can also act on different target subgroups to cause differentconversions. In general, the population conversion model provides thefull description of the target populations which lose members, thepersuasive forces functions causing the conversions, and the destinationpopulations whose numbers are increased by the opinion changes.

The details of the population conversion models will depend on the totalnumber of polled positions, the total types of infons, and the changeswhich are likely to occur. In the case of the Democratic primary, themost complex example in this book (Chapter 5), there were four polledpositions (pro- Mondale, pro-Glenn, pro-Others, and No Opinion) and sixtypes of infons (pro-Mondale, pro-Glenn, pro-Others, con-Mondale,con-Glenn and con-Others).

Once the infons have been scored and the results used to constructpersuasive force functions and once the population conversion modelshave been formulated, public opinion is calculated using ideodynamicequations. These equations, applied to mass media infons, are based onthe following principles:

(1) The number of people converted is proportional to the persuasiveforce functions.

(2) The number of people converted is also proportional to the number ofpeople in the target population. In the extreme case of everyone alreadyholding the opinion of the infon, there will be no non-believers andhence a non-existent target population. Then, there will no be convertsregardless of the strength of the persuasive infons.

(3) The constant of proportionality is the "persuasibility" constant(Appendix A, Equation A.14). This constant reflects the fact that someattitudes are closer than others to the core beliefs of an individual.Very firmly held attitudes will be much more difficult to alter. Thus,the persuasibility constant for defense spending will be larger thanthat for religious beliefs which are probably much more refractory tochange. The difficulty of changing religious beliefs is seen in thetight correlation between religious beliefs across generations(Cavalli-Sforza, et al., 1982).

The persuasibility constant is related to the volatility of opinion andto the malleability of the population under the influence of newinformation. Both opinion volatility and population malleability arelarge for issues with high persuasibility constants.

Based on the studies in this book, there can be wide differences in thepersuasibility constant depending on the issue.

(4) In addition, ideodynamics also includes "refining weight" constantsto account for the fact that not all opinion conversions from one targetsubpopulation to another proceed by exactly the same persuasibilityconstant. In effect, the persuasibility constant can be considered to bethe coarse adjustment and the refining weights to be the fineadjustments since the persuasibility constant could differ 50-fold fromissue to issue while the refining weights varied less than 3-fold forany one of the six issues tested (Table 5.2). As discussed in AppendixA, the refining weights also include adjustments to account forvariations in message scoring. modified persuasibility constant, sethere at the unrealistically large value of 2000 per AP paragraph per dayto demonstrate the shapes of the curves. All refining weight are assumedto be the same and are set to 1.0. Also, the time between calculationsis the very short interval of 2.4 hours to illustrate the fine structureof the curve.

In summary, the general rule in ideodynamic calculations is that allopinion conversions increase with the sizes of the target subpopulationsand the magnitudes of the persuasive force functions which depend inturn on the infons' contents, validities, and audience sizes. Thedurations of the audience size functions for AP infons are governed bythe persistence constant.

As a result, opinion calculations only depend on a persistence constantwhich is common for all issues, a modified persuasibility constantdifferent for every issue and refining weights which usually have thevalue of 1.0. In the simplest cases, like that for defense spending(Chapter 5), the modified persuasibility constant was the onlyindependent constant which needed to be fixed since the initial defaultassignment of 1.0 for all refining weights was satisfactory.

It is the empirical testability of the model and its success in six outof six cases which permits the suggestion that this parsimonious modelis both robust and highly predictive.

In ideodynamics, persuasive messages can both reinforce opinion and losetheir effect due to over-repetition as discussed above. However, ininitial computations, it was assumed that neither reinforcement norsaturation was important. Since these minimal approximations gave Goodopinion projections, they are the ones used for the results in thefollowing Chapters.

1.8 Details of Opinion Calculations for Unawares

The previous section has considered the special case where the entirepopulation consisted of awares. If unawares are also added, then theequations will change somewhat because the unawares cannot rememberinformation which did not lead to awareness of the issue. In addition,only direct information can act on the unawares as noted in Section 1.4so indirect infons are not included in persuasive force functions forthe unawares. The details of the mathematics for the unawares arepresented in Appendix A.

Upon acquiring awareness, the unawares can adopt one of the positions ofthe awares or become aware and undecided, with the choice of these twopossibilities depending on the details of the population conversionmodel for the unawares. Rogers (1983), for example, prefers the movementof the unawares first into the group of aware but undecided beforesubsequently adopting a position. However, the direct adoption of aposition might also occur.

As with opinion conversion, the acquisition of awareness is assumed tobe proportional to the relevant persuasive force functions and thetarget population size. However, the constant of proportionality will bedifferent. This constant is referred to as the "artentireness" constant(Appendix A) and recognizes that the population may be more or lessattentive to the issue being considered.

Besides acquiring awareness, the population can also forget about anissue so the ideodynamic equations also include a loss of awareness dueto forgetting (Appendix A).

For the cases in this book, the entire population could be considered tobe aware of the issue since the number of Don't Knows in the pollresults was usually less than 10%. Therefore, there was no need to modelthe conversion of the unawares to awareness.

1.9 Time Scale of Ideodynamic Analyses

The equations of ideodynamics discussed so far have all assumed thatmembership in the population does not change during the entire time spanof the analysis. Thus there is assumed to be no birth or death and nomigration either into or out of the population. These approximationswere satisfactory for time intervals up to the 9 years studied fordefense spending (see later chapters).

As times increase to generations and centuries, modifications will beneeded in the model. At a minimum it will be necessary to account forbirth and death. Death can easily be modeled by the loss of awares usingterms similar to those describing forgetting (Appendix A). Birth can bemodeled by the introduction of new unawares into the population. Theacquisition of awareness by has been described in the previous section.In accounting for birth and death, corrections will also be needed inthe ideodynamic equations if the population changes size.

Besides birth and death, long term opinion calculations over generationswill also need to account for the fact that the constants in theideodynamic equations might change slowly with time. For instance,public belief in press trustworthiness might diminish if evidence ofunreliability is presented.

In practice, however, the public is concerned with a large number ofissues only within time spans substantially shorter than a generation.For example, the issue of the Democratic primary of 1984 (see laterchapters) was not important for more than a few months. Therefore, thisbook has not considered opinion changes over very long time spans.

Although the emphasis in this Chapter has been on public opinion,ideodynamics can also be extended to other social traits like habits(Fan, 1985b).

The next Chapter contains a summary of the principal features ofideodynamics in the context of other models.

FIG. 1.1 Example of Persuasive Forces of Infons

The population was assumed to be exposed to two AP dispatches at thebeginning (top frame) and two days after the beginning (center frame) ofweek 0. The information in each dispatch is separated into infonsfavoring different polled positions. This figure plots the infons fromthe two dispatches favoring one of the several possible positions. Thetop two frames give the effects of the two infons separately with a oneday persistence half-life. The combined effect (bottom frame) is the sumof the individual forces. The units for the infon force curves areaverage AP paragraphs. In this example, both the first and second infonshad values of one paragraph on the their emission date.

FIG. 1.2 Population Conversions Model for Defense Spending

The boxes denote the subpopulations under consideration. The words inthe boxes begin with B to refer to those "believing" or having anopinion favoring more, same, or less spending. The calculations for thepersuasive forces G are given in Appendices A and D. For this figure,infons scored as supporting more, same and less spending are used tocalculate persuasive force curves supporting the same three positions(GMore, GSame, GLess). The tail of an arrow indicates the targetpopulation which loses numbers by opinion conversion due to thepersuasive forces over the arrow. The head of the arrow points to thepopulation gaining members from the conversion.

FIG. 1.3 Illustration of the Impact of a Single Persuasive InfonFavoring More Defense Spending

A single infon with a maximum value of one AP paragraph was assumed toarrive at the population at week 0. The infon's persuasive force (topframe) has the same shape as those in FIG. 1.1. The population at -1weeks is presumed to be evenly distributed among those favoring more,same and less defense spending. The effects of the infon in the topframe on public opinion are drawn in the three lower frames using thepopulation conversion model of FIG. 1.2 and assuming a modifiedpersuasibility constant of 2000 per AP paragraph per day.

CHAPTER 2 Ideodynamics and Previous Models

The previous chapter examined the formulation of ideodynamics. Thepresent chapter considers the principle features of ideodynamics in thecontext of other models for social change. As noted in the Introduction,and as will be discussed again at the end of this chapter, an importantgoal of ideodynamics was to provide a framework encompassing seeminglydisparate conclusions. Throughout this discussion, special attentionwill be paid to three features of ideodynamics which are unique in beingpresent simultaneously: empirical testability, parsimony, and equationsgrounded in real time.

2.1 Significant Features of Ideodynamics

One of the unusual aspects of ideodynamics is its capacity to predictopinion trends for which the time intervals of the computation arearbitrarily small. As a result, the computed trends can reflect rapidopinion changes.

The 6 or 24 hours used for the examples in this book, were chosenbecause it seemed unreasonable to calculate at intervals much shorterthan 6 hours since the message and opinion poll data were not known toany greater accuracies. Twenty-four hours was used for long time trendslasting longer than one year in order to decrease the time needed forthe computations.

The logistic equation is the best known other example of a calculationfor social change where the time interval of calculation can be ofinfinitesimal size (Bartholomew, 1976; Fan, 1985a; Hamblin, et al.,1973).

Other investigators who have explicitly included time in their modelshave usually used time intervals ranging from weeks to months.Obviously, the longer the time interval, the less precise will be thecalculations of public opinion or any other social response.

As the time interval diminishes, messages appearing in one time intervalwill continue to exert their influence in the next time interval. Toaccount for this phenomenon, time lags have been invoked for thecontinued persuasive force on messages. As for this book, the laggedinformation has typically been assumed to decrease geometrically orexponentially over weeks or months (e.g. Hibbs, 1979; Ostrom and Simon,1985).

Besides permitting opinion calculations over time intervals as short ashours, ideodynamics is also testable empirically. For such tests, it isessential that the number of parameters in the model be small withrespect to the number of predicted values which can be compared withempirical data. Given enough variable parameters, a general model mightfit any set of data. In ideodynamics, it is possible to obtain a verylarge number of computed values by calculating opinion time series. Infact, there is no theoretical limit to the number of points which can betested because a new set of values is predicted as soon as another timeinterval is added. A new interval can be added either by extending thetest time or by subdividing the original test interval into smallersubintervals. By creating a new time interval, the constants in theideodynamic equations do not increase. Therefore, after fitting theparameters to a few time points, it becomes impossible to adjust theconstants to fit later data points. Additional poll values will thentest the model critically since the constants will already have been setand can no longer be adjusted.

If the model gives good fits for a large number cases, despite thepaucity of parameters, then the model will have been shown to be bothgeneral and robust. It was to explore generality and robustness thatideodynamics was tested with six examples using the simplest formulationwith the minimum number of parameters. The critical empirical testing ofideodynamics then takes advantage of time series with hundreds orthousands of time steps and a similar number of predictions (Chapter 6).

Besides being able to study the sufficiency of very few parameters, thetestability of ideodynamics also permits bold simplifying approximationsin the choice of relevant persuasive messages. Therefore, AP messagesalone were assigned to represent all mass media messages.

This justification is based on the structure of American news diffusion.In the United States, the written press read by the majority of thepopulation is locally based. A relatively small percentage of thepopulation will get their news from either news magazines like Time orNewsweek or newspapers with national circulation like the New YorkTimes, the Wall Street Journal or USA Today.

Most local newspapers do not have the resources to have their ownreporters on the national or international scene. Therefore, thesepapers rely on the wire services for their coverage of non-localnews--news for all of the topics in this book. For these topics, then,most readers read material coming directly from the wire services. Amongthese, the AP is clearly dominant. Given its prominence and its verywide distribution, the AP also takes tries to take neutral positions sothat its stories will be acceptable to publishers with differentpolitical preferences.

The other common source of news for the population is the electronicmedia which were not included in the analyses in this book. Thisomission was due to the difficulty in assessing TV and radio news. Itmight have been possible to use the news summaries in the VanderbiltTelevision News Archives (Chapter 3). However, these summaries wereextremely brief and did not give a complete idea of broadcast content.

As a result, the approximation was made that AP stories could alsorepresent news in the electronic media even though those stories werenot quoted verbatim in news shows. Common observation of the similarityin news from the AP and electronic broadcasts suggests that thisapproximation is also plausible. Indeed, Paletz and Entman (1981) havereported that there are frequently great similarities in reports fromvarious segments of the mass media.

With these justifications, the AP alone was used to represent allnational and international news from both the written and electronicpress. Given the testability of the model, this approximation could atleast be tried. If it was invalid then inaccurate opinion time trendswould have been calculated, and it would be known that one or moreaspects of the model, including the choice of news source, was faulty.The problem could be traced to the choice of the AP if other choices forpersuasive messages gave better calculations.

On the other hand, accurate computations verified empirically for alarge number of issues would suggest that the model is predictive andthat AP news is sufficient for opinion calculations despite thepeculiarities of each issue.

One significant aspect of ideodynamics is its disaggregation of thepopulation. The result was a non-linear model in which opinion changewas due to the the product of persuasive force functions and targetpopulation sizes. In contrast, alternative linear models have beenproposed where relationships are drawn between opinion and informationwithout subdividing the population.

Among the influential studies in the persuasion literature, forinstance, are those by Funkhouser (1973a,b) reporting that the importantitems on the public agenda correlated well with mass media coverage overa seven year period. In the recent study of Ostrom and Simon (1985),Presidential popularity was calculated from a number of factorsincluding legislative success and activity in the domestic field,foreign policy, economic prosperity, war, sympathy, and unanticipatedvariables. At monthly intervals, the factors were computed andcorrelated directly with Presidential popularity. Some information waspermitted to act in a lagged fashion extending from month to month in adecreasing fashion. Still on the topic of Presidential politics, Markus(1982) has examined the effects of party identification, perceptions ofcandidate traits and incumbent performance dissatisfaction on politicalcandidate evaluations during a period of about one calendar year. Sinceone of the cases in this book concerns inflation versus unemployment asa priority problem it is useful to note that Hibbs (1979) has alsostudied the relationship between public perception of the relativeimportance of these two problems in relation to contemporaneous and pastbehavior of the economy in these areas.

This is but a partial list of studies in which opinion is calculatedfrom the information structure alone without subdividing the respondingpopulation and modeling the differential action of different informationon different subpopulations. One of the logical consequences ofcalculating opinion from the information structure alone is that thecomputation yields the same results regardless of the populationstructure before the calculation time.

An analysis of the ideodynamic equations (Appendix A) shows that thesecalculations are equivalent to the ideodynamic equations being evaluatedunder conditions where the population is at equilibrium with the newsstructure. In other words, all changes caused by persuasive messages areassumed to have occurred. For defense spending, for example, that wouldmean that the same opinion would have been found in the time interval ofcalculation regardless of whether the population in the previous timeinterval consisted of only those favoring more spending or only thosesupporting less spending. This approximation becomes progressively moreinappropriate as the time interval of computation decreases since atvery small time intervals, such as the 6 or 24 hours used in this book,it is rather unlikely that opinion would totally reflect the messages inthat restricted time period. Ideodynamics is able to overcome therestriction of assuming population equilibrium by subdividing thepopulation and modeling opinion formation via change in opinion from aprevious time.

One of the important considerations underlying the formulation ofideodynamics was that it should be possible to separate message creationfrom message impact. This separation recognizes that the essential linkbetween message generation and message impact is the message itself. Thepurpose of this book was to demonstrate successful tests of the part ofthe model dealing with message impact. Success here would mean that thestructures used for coding messages could incorporate all the crucialfeatures of persuasive information needed for explaining message effect.As a result, a full understanding of persuasion would be obtained froman analysis of how messages were generated once message effect could becomputed.

Message structure in ideodynamics is based on infons and theirproperties. The approximation was that messages could be separated intoinfons using the four dimensions of position favored, directness,message source, message medium, and message index number. The onlyrelevant features of infons postulated to be essential to persuasivewere the content scores, validity scores and audience size functions.Successful empirical tests would suggest that models for messagegeneration could stop with the coding of messages as infons.

In fact, models for message creation have already been examined wherethe output messages are structured as infons. One example is theideodynamic derivation of the logistic equation for the diffusion ofinnovations. For this equation, it is assumed that infons favoring theadoption of social innovations are generated in proportion to the numberof people who have already adopted that innovation. This would be thecase if all adopters of an innovation would have shown and told othersabout the innovation with approximately the same effectiveness. Thisapproximation, together with the approximation that the non-adopters didnot send messages opposing the innovation, directly leads to thelogistic equation (Fan, 1985a).

In another instance of the addition of a message generation step toideodynamics, it was found that there would be no change in the ratiosof people for and against a position if partisans of both sidesbroadcast their views with the same efficiency (Fan, 1985a). Althoughthis result may not seem obvious at first glance, it is reasonable whenthe analogy with genetic systems is examined. The ratio of colorblind tonon-colorblind people will stay constant if both groups can reproduce asefficiently and if both groups have the same life expectancy.

These two examples demonstrate that infons can provide the neededstructure to model message generation as well as message impact.

Part of the usefulness of infons for modeling both message synthesis andmessage impact lies in their flexibility. For instance, infons and infonpersuasive force functions also permit the modeling of the reinforcingeffects of the media and information saturation due to message overload.Ideodynamics is unusual in being able to include these phenomenaexplicitly in the mathematics (Chapter 1 and Appendix A). This is doneby permitting reinforcing infons to attenuate the persuasive forcefunctions of infons acting to convert the reinforced individuals. At thesame time, saturation by conversion infons could also lead to decreasedeffectiveness of the conversion infons themselves. However, based onempirical tests, it appeared that there was no significanttime-dependent opinion reinforcement (Chapter 7).

The flexibility of infons is also seen in their capacity to provide auniform structure which is readily adaptable for analyses of informationwith persuasive components lying in many directions such as supportingand/or disadvantaging one or more of three groups of Presidentialcandidates (see following chapters). This ability to consider severaldifferent positions simultaneously is difficult to do with schemes suchas those of Page and colleagues (1983a and b, 1984, 1985, 1987) in whichcommunications are scored on a directionality scale from pro to con.

Important advantages also derive from the non-linear aspect ofideodynamics resulting from the multiplication of persuasive forcefunctions by the sizes of appropriate target subpopulations. Oneinstance is the ability to overcome implicit restrictions on populationstructure. To illustrate, consider mixed messages in the simple case ofonly two positions, pro and con. A concrete example would be warningnotices in cigarette advertising. The message here is clearly mixed. Themanufacturer's component is favorable to smoking while the warningnotice is not. To simplify the analysis, assume the hypothetical casewhere the pro- and anti-smoking messages have equal persuasive force. Inthis case, the directionality would be neutral using the method of Pageet al. (1983a and b, 1984, 1985, 1987). In their calculations, such aneutral message should not affect public opinion.

To examine the situation more closely, consider a population consistingentirely of smokers. For such a group, the anti-smoking component of themessage might make some of the members want to quit. Whether they wouldactually quit is explored in the extension of ideodynamics to habits(Fan, 1985b). Therefore, a neutral message with an anti-smokingcomponent is likely to win converts to the non-smoking cause when thepopulation consists only of smokers.

Conversely, if the population consisted only of non-smokers, a certainnumber of these persons would want to start when faced with a messagewith a pro-smoking component. This analysis demonstrates that neutralmessages are not necessarily neutral since population shifts can occur.Instead, neutral messages can cause an increase in either the smokers ornon-smokers--depending on the starting population. Therefore, thepersuasive effects of messages cannot be calculated in the absence ofknowledge of the starting population.

If this population does not consist of only smokers or non-smokers butrather a mixed group, then the arguments just presented will apply tothe individual subpopulations. If the pro- and anti-smoking messageswere of equal force and if each group of messages is able to persuadeone percent of their target subpopulation, then one percent of thesmokers should want to quit while one percent of the non-smokers shouldwant to start. Only with equal numbers in the two groups would there beequal traffic in both directions resulting a net change of zero in thedesire to smoke.

Therefore, if directionality is the only measure for persuasivemessages, and if population shift is correlated with directionalityalone, the implicit approximation is that the population is sufficientlyclose to fifty-fifty in favor of pro and con that there is anapproximate net balance between the movement from pro to con and con topro. Ideodynamics does not make this approximation. Instead, the sizesof the target subpopulations are entered directly into computations ofopinion movement--from both pro to con and con to pro--using products ofthese population sizes and their corresponding persuasive forcefunctions.

The adapability of ideodynamics is further demonstrated by its abilityto examine the frequently mentioned concept of opinion leadershiporiginally proposed by Lazarsfeld, Berelson and Gaudet (1944) (see alsoCampbell, 1979; Katz, 1957; Katz and Lazarsfeld, 1965; Weiss, 1969). Inthis concept, information in the mass media is postulated to act firston opinion leaders who then make the transfer to the rest of thepopulation of ideas is that of the opinion leader.

Ideodynamics can treat opinion leadership in two different ways. In themost complete, the total population would first be divided intosubpopulations of opinion leaders and followers. The analysis wouldfirst be made for the impact of media messages on the leaders. Then eachinfon generated by the opinion leaders could be modeled for theircontent, source, and audience size. Finally, ideodynamics would considerinfons from the opinion leaders acting on the population as a whole.Such an analysis could be quite complex requiring the inclusion ofsignificant imponderables such as the identification of the opinionleaders for particular issues and their infon generation patterns.

However, there is an alternative method for including opinion leadershipbased on an analysis of the functions of the opinion leader. If mediamessages are not substantially distorted by the opinion leaders duringthe two step transfer, then these individuals could be considered to beamplifiers of the message. A significant amount of time might be neededfor the opinion leaders to retransmit messages. This would only enterinto the ideodynamic calculations by shifting the audience size curve tolater times assuming that the original source of the infons was the massmedia. Therefore, if most media messages were retransmitted accuratelyby the opinion leaders and if the retransmission had a characteristictime delay, then the mass media could still be considered to be theoriginal message sources with the effects of the opinion leaders beingabsorbed into a time delay in the audience size curve for mass mediainfons.

From this argument, the decrease in audience size after the transmissionof a mass media message will give an idea about the likelihood of theimportance of opinion leaders responsible for a second step transfer ofmass media information. In the studies in this book, the effects of allmass media infons disappeared exponentially with a one day half-life.Therefore, any delays due to opinion leadership must occur within a veryfew hours.

So far, the stress has been on the ability of ideodynamics to permitdifferent information to have different effects on differentsubpopulations. Another type of population heterogeneity tolerated bythe model involves non- uniform information transfer. For example, thereis no difficulty with people preferentially exposing themselves toinformation compatible with their own system of beliefs (Klapper, 1960;Sears and Freedman, 1967; Campbell, 1979). Since such information shouldreinforce beliefs, preferential exposure is reflected in the constantmultiplier describing the persuasive power of the reinforcing infons(Appendix A, denominator of Equations A.12 and A.13). If allsubpopulations are as avid in avoiding information favoring otherpositions, then the same constant can be used for all reinforcinginfons.

It is also unnecessary that every member in the population have the samechance of receiving all infons. The model only requires that an infon'schances of reaching more people increases in proportion to the number ofcopies of the infon released, regardless of an infon's geographiclocation or other special broadcast circumstances. For instance, oneclass of infons, those from personal experience, will only affect theperson who had the experience. It is sufficient that more individualswould receive personal experience infons as more are Generated--whichwill certainly be the case.

This class of infons is obviously just one example of the Geographiclocalization of persuasive messages. Here, the sender and receiveroccupy exactly the same site. In a less extreme case, persuasivemessages might Generally be localized to the region of the sender as hasbeen found by Haegerstrand (1967) for the diffusion of such agriculturalinnovations as the antitubercular vaccination of cattle in Sweden.Again, there is no difficulty so long as more information means morepeople having a chance of becoming exposed. It is only important thatthere be no large population isolates where the information available tothe general population penetrates with either much Greater ease ordifficulty. If the isolated populations are small, then their effectscan be ignored.

In ideodynamics, there is no statement of the amount of time that aperson needs to stay in a particular subpopulation before undergoing anopinion change. Therefore, a person might move from favoring lessdefense spending to the position of supporting more spending almostinstantaneously. The model only states that at least two infons arerequired for this conversion while only one is required for the movementfrom less spending to same spending.

2.2 Model Comparisons

Having discussed some of the important features of ideodynamics it isappropriate to compare this model with others. A reasonable startingpoint might involve the topic just treated, namely heterogeneitieswithin the population. First of all, a variety of other mathematicallybased models do not discuss the question of population inhomogeneitiesas has been done above (Allen, 1982; Bartholomew, 1976, 1981, 1982;Bentler, 1980; Bentler and Speckart, 1979; Brams and Riker, 1972;Cavalli-Sforza and Feldman, 1981; Cavalli-Sforza, et al., 1982; Coleman,1964; Cook, et al., 1983; Daley and Kendall, 1965; Goldberg, 1966; Grayand von Broembsen, 1974; Huba and Bentlet, 1982; Huba, et al., 1981;Karmeshu and Pathria, 1980a,b; McIver and Carmines, 1981; Sharma, etal., 1983)

Turning to less mathematical models, ideodynamics is consistent with theidea that at any one time there will be "innovators" and "laggards"(using Rogers and Shoemaker's, 1971, terminology) among the remainingindividuals who have not been converted. The requirement in ideodynamicsis only that the same proportion of a subgroup should be recruited, atall times, upon contact with an infon of a particular strength. Thusthere would be no conflict with laggards becoming gradually morepersuasible as additional infons favoring change are sent to the targetsubpopulation and as the innovators are convinced to change their minds.

The separation of a subgroup into laggards and innovators is but oneexample of dividing a subpopulation into members ranging from oneextreme to another. Obviously, different results are expected whenevermeasurements are made on persons at one extreme or another, regardlessof the dimension used for scoring the individuals.

For instance, Coleman, Katz and Menzel (1966) measured the time ofadoption of a new antibiotic among physicians with different socialtraits. The percent making the adoption was charted over a period ofabout a year and a half for two subpopulations at the low and highextremes for characteristics such as attendance of meetings,subscriptions to journals, social participation in the the medicalcommunity, etc. The most sensitive time for comparing the twopopulations is that at which 50% adoption occurred since the steepestrate of increase typically occurs at this time. The time for 50%adoption usually differed by two to five months for extreme subgroupsdepending on the social characteristic used for partitioning thepopulation. However, the general shapes of the adoption curves alwayshad the typical S- shape characteristic of the logistic equation. Thecurves began just after the introduction of the antibiotic, hence 0%adoption, and finished at approximately 100% with the period requiredfor the total change being in the range of a year and a half to twoyears. In general, the early adopters were physicians with the greatersocial interactions.

Given the similarities in the shapes of the curves for subpopulationswith extreme characteristics, the curve for the total population wouldalso have had the same logistic shape somewhere between the curves forthe population extremes. In other words, the population treated in thesestudies was probably sufficiently homogeneous that logistic plot couldapply to the population average without denying the existence ofpopulation heterogeneities.

If desired, ideodynamics can also be applied to any subpopulation of thetotal. In this book, for example, the unawares were ignored. This is oneof the advantages of the introduction of infons. The analysis involvesdefining the infons acting on each of the subpopulations remaining inthe analysis and then describing the action of these infons on a singlesubpopulation or group of subpopulations, a step which can beaccomplished with no changes in the model.

It should be noted that any population, however small, can always befurther subdivided to make even finer distinctions among the individualmembers until the subpopulations are so small as to have only onemember. So long as a subpopulation is larger than one person, almost allsocial science models assume that the subpopulation is sufficientlyhomogeneous that some generalizations can be made. Thus when Coleman andhis colleagues drew time trends for doctors who attended four or moremeetings the approximation was that these physicians were sufficientlyhomogeneous that their data could be pooled to give a common curve.

In brief, Rogers and Shoemaker's separation of laggards from innovatorsmakes generalizations about these subgroups. Similarly, Coleman and hiscolleagues draw generalizations from their physicians pooled by socialtraits. The broad generality in ideodynamics about population behavioris to assume that the percentage recruitment from a target population isproportional to the size of that subpopulation. Fortunately, thisapproximation permits certain population heterogeneities and means thatideodynamics is compatible with the results of other investigators, asjust demonstrated and as discussed in the previous section.

In addition to being compatible with other models from the standpoint ofthe treatment of population heterogeneities, ideodynamics can alsoencompass other models. One group would include models for social changein which time does not appear explicitly in the analyses and in whichthe information passing from information sender to receiver is notmeasured but deduced. Some of these models have emphasized the pathwaysby which social decisions occur within a single individual. Forinstance, Rogers (1983) has proposed that knowledge of an innovation isfollowed by persuasion before a final decision to adopt. He has alsonoted that innovators adopt an innovation earlier than laggards. Thesteps in decision making have also be studied by Bentler and colleagues(Bentier, 1980; Bentler and Speckart, 1979; Huba and Bentler, 1982;Huba, et al., 1981) based on modifications of the model of Fishbein andAjzen (1975). These authors have proposed for chemical dependency thatan individual typically moves from usage of alcohol to cannahis to harddrugs like heroin.

As already discussed, the system of social networks through whichpersuasive information passes has been studied extensively by Coleman,Katz and Menzel (1966) and Granovetter (1973, 1978, 1980) among others.Here, the emphasis was on who was likely to interact with whom andsuggested that innovations first entered social networks through weakinteractions with other groups. Then, the innovation spread rapidly uponpenetration into a group of tightly knit homophiles comprised of closelyinteracting individuals sharing common traits.

Since studies on the pathways of social interactions and decision makingfrequently use data from one time surveys, the messages and theirassociated infons are not measured directly. Instead inferences are madeabout the transmitted messages. For instance, awareness of innovationswere assumed to be due to messages arriving at the population at thetime that awareness occurred; messages favoring hard drug consumptionwere assumed to be correlated with cannabis usage; and additionalmessages about an innovation were presumed to be transmitted asinterpersonal communications occurred in a social network.

With its emphasis on real time, ideodynamics is generally consistentwith models describing the steps just described for social changes. Anideodynamic analysis would formulate the implied messages in terms ofinfons and would add the crucial element of real time.

For instance, if the time from awareness through persuasion and decisionis relatively rapid then the population conversion model and itsassociated equations would treat these two steps as occurringessentially simultaneously with persons moving directly from unawarenessto adopting a position. On the other hand, some decisions might indeedtake a long time. For instance, the time between awareness and adoptionof the innovation of 2-4D weed spray among Iowa framers was in the rangeof years (Beal and Rogers, 1960). In this case, the populationconversion model would reflect movement of the unawares first into thepool of aware but undecided and then into adoption of the innovation.For cases where economic decisions are involved, the ideodynamicequations for habits (Fan, 1985b) might be more useful.

Yet other time independent studies have been performed where subjectsare exposed to actual persuasive communications in a laboratory settingwith the characteristics of the population being measured both beforeand after the exposure. Using this protocol, for instance, Iyengar etal., (1984) have suggested it is more difficult to influence expertsthan novices through television news on the subject of energy policy.Ideodynamics suggests that these studies might benefit from aconsideration of the time needed for persuasion. For instance, it may beinformative to compare the results of survey questionnaires at varioustimes after the exposure to television news rather than only once suchas immediately after information exposure. If there is any lag in thedecision making process, say from awareness through persuasion todecision, then the time of the post informational measurement might beof great importance.

Besides social science descriptions in which real time is not explicitlyincluded, there are other models in which time is explicitlyincorporated. In one class of these models, persuasive messages are notexplicitly described as is done in ideodynamics. Instead, the messagesare inferred from the characteristics of the message senders. Of these,perhaps the most successful has been the epidemiologically based modelyielding the logistic plot for the diffusion of innovations (seeprevious Section). This equation derives from assuming that all peopleadopting the innovation generate favorable infons equally. In a morerecent variant, Sharma, Pathria and Karmeshu (1983) postulate that somepeople receiving a message about an innovation are "stiflers" who do notgenerate favorable infons.

Another model in which infons are deduced from the structure of messagesenders is based on genetics (Cavalli-Sforza and Feldman, 1981). Herethe messages from parents (vertical transmission) are assumed to haveforces different from those from peers (horizontal transmission) andteachers (oblique transmission). This model was formulated in terms ofgenerations and is therefore most appropriate for slow movingtransmissions of social traits such as culture or religion whichtypically change very little within a generation (Cavalli-Sforza, etal., 1983).

From a historical perspective, it is understandable that models based onepidemiology and genetics do not explicitly include measured messagesbecause the factors responsible for change in these areas are not easilydetermined directly. For example, the logistic curve is based onepidemiological models of infectious diseases where the infectiousvirus, bacteria or other microorganism usually cannot be traced duringtheir movement from an infected individual to a new victim. Similarly,the transmissible agents in genetics are the genes in the sperm and eggswhich, again, are not easy to measure in the natural population.

Ideodynamics can also encompass other models with implied messages byexplicitly coding the implied messages as infons (Fan, 1985a, andChapter 7). Yet other models are like ideodynamics in including realtime and measured messages. The key advantage of measured messages isthat they reflect the uncertainty in the real world. However, newmessages can never be predicted with certainty, so calculations ofsocial change based on actual messages can never extend beyond the timewhen the messages are available.

Since the crucial aspect of measured messages is that their compositionscannot be predicted ahead of time, measured messages for this discussionwill include not only direct messages but also those presumed to becorrelated with historical events. The main constraint is that themeasured messages should not be deduced solely from the structure of thepopulation. Therefore, some measured messages can be quite direct suchas newspaper articles when scored, for example, as described above byPage and Shapiro (1983a,b). Other measured messages might be moreindirect such as those correlated with historical facts such asroll-call votes in Congress (Ostrom and Simon, 1985). The public is notlikely to be aware of such votes. However, indirect messages arisingfrom such votes might indeed be disseminated to the public. Ideodynamicshas the advantage over models like these by subdividing the populationbefore computing opinion (see previous Section).

Time dependent models by other authors in which messages are explicitlymeasured include the agenda-setting and persuasion models by Erbring,Goldenberg and Miller (1980) and MacKuen (1981, 1983, 1984). It hasalready been shown that these models can all be considered to be specialcases of ideodynamics (Fan, 1984). Like ideodynamics, these models alsodivide the population into different subpopulations.

Throughout the previous discussion of other models, an effort was madeto see if previously reported phenomena were inconsistent withideodynamics. It was reassuring that this model is compatible with theseother social science models, some superficially disparate. Therefore, asnoted in the Introduction, ideodynamics is not so much a competing as anumbrella model--the equivalent to the elephant in the elephantanalogy--with a structure suitable for incorporating the details of manyof the other models discussed above.

Important novelties of ideodynamics include the incorporation real timein the analyses, the existence of the logistic equation as a specialcase and the explicit mathematical modeling of opinion reinforcement andother message interactions. Any alternative models should also be ableto account for all these features simultaneously. The capacity to derivethe logistic equation is especially important given its ubiquitoususefulness in explaining social science time courses. The importance ofconcurrent studies in real time of a number of measurable socialvariables--media messages and public opinion in this book--has also beenstressed by Neuman (1987) in his extensive review of the persuasionliterature.

CHAPTER 3 Data for Calculating Public Opinion

The theory in Chapter 1 provided a method for calculating the results ofopinion polls in a time dependent fashion given the informationavailable to the public. It was also demonstrated that this model couldbe tested using two types of data, a time series of public opinionpolls, and a representative sample of the information available to thepopulation for each polled issue. This chapter concerns the actual dataused for the empirical testing of ideodynamics.

3.1 Time Series of Opinion Polls

As discussed in the Introduction, one of the key goals of this book wasto assess the generality of ideodynamics. Therefore, it was important tostudy issues which were varied and disparate in nature. As a result,case studies were performed for two foreign policy issues, a domesticpolicy issue, two economic issues and an electoral campaign.

One important criterion was that the information influencing the publicshould be readily available for study. It would obviously have been verydifficult to calculate public opinion from persuasive messages if theycould not be captured for analysis. Since the public record was mostcomplete for the news portion of the mass media, the decision was madeto concentrate on issues where this was the source of the majority ofthe relevant messages.

The most convenient method for obtaining mass media messages was toretrieve them from an electronic data base such as the Nexis data basesold by Mead Data Central of Dayton, Ohio. Since the data base extendedback to 1977 for the Associated Press (AP) dispatches used for thesestudies, the decision was made to study only issues with poll timeseries since 1977. As argued in Chapter 2, the Associated Press waslikely to be representative of both the written and electronic press.

Another consideration was that the poll points from most time seriesshould have changed significantly during the polling period since verysimple models could predict no change at all in poll results. Therefore,the most dramatic tests involved polls with marked opinion changes.

Parenthetically, the condition of opinion change meant that the publicwas reasonably persuasible for those issues where change was found. Bydefinition, public opinion would have stayed constant for issues forwhich the public had very firm convictions. For instance, the abortionpoll series since 1977 with the largest number of time points was onefrom NBC news from 1977 to 1982. In this series, the widest opinionswings were only 5 to 10 percent (data from B. I. Page and R. Y. Shapirocompiled at the National Opinion Research Center (NORC) in Chicago).

Five issues were chosen for careful analysis given the conditions of (1)illustrative of issues in many domains, (2) many times points since1977, (3) significant opinion changes, and (4) most relevant persuasivemessages coming from the mass media. The polls (see Appendix B fordetails) were mainly from the extensive compilations of time series madeby Page and Shapiro at NORC (see Page and Shapiro, 1982). Forcomparison, another test issue involved opinion on Contra aid for whichopinion stayed reasonably constant. For this issue, the poll series wereobtained from the Roper Center at the University Of Connecticut.

One of the most interesting poll series was that of public opiniontoward the advisability of more, same or less defense spending. Theimportant persuasive messages for this topic were most likely to belocalized in the mass media since the relevant considerations wereconstantly changing. All other methods such as books were too slow toreflect these fluctuations. The public certainly could not calculate theamount of money which should have been spent on defense by makingcalculations from a set of general principles. Very few people with anopinion on defense spending even knew what the defense budget actuallywas.

The Page and Shapiro compilations contained four poll series from1977-1984 (Table B.1). These time series were all pooled into onebecause a common curve could be drawn through all the points (FIG. 3.1).However, the reader can concentrate on any one data set since each timeseries is denoted by a different symbol. Initially, opinion projectionswere made for 1977-1984. Later, a test was made for the ability toextend the text analysis and opinion calculations to 1986. For thesestudies additional published polls were obtained from the Roper Centerat the University of Connecticut yielding a total time series with 62polls (Appendix B).

The defense spending issue was interesting because public opinionunderwent a large increase and then a marked decrease. However, thechanges were slow. During all of 1979, those favoring more defensespending rose dramatically from 20-30% to approximately 70%. Opinionthen remained high high for another year before the one year drop backto 20-30% in 1981.

Another issue with striking opinion shifts was whether more Americantroops should have been sent to Lebanon in 1983-1984 (Table. B.2). Thetroops had been sent as part of the Multinational Peacekeeping Forceafter Israel withdrew from Lebanon in 1982. In contrast to the defensespending example, only 2 to 3 months were needed for people in favor ofsending more troops to change from 7% up to 31% and back down to 9%.

Together, troops in Lebanon and defense spending permitted the testingof the computational methods for dramatic opinion changes spanning daysor months.

The two previous examples of defense spending and troops in Lebanonconcerned policy issues. The next topic involved political popularity inthe 1984 Presidential election (Table B.3). The choice was made to studythe Democratic candidates because none had the extra advantage ofincumbency. Also, the analysis ran from 1983 to 1984 stopping just shortof the first real test of strength, the Iowa caucuses in 1984.

After that time, relevant information became less and less restricted tothe mass media with candidate advertising and other campaign messagesbeing progressively more significant. Unfortunately, these non-newsmedia messages were much more difficult to obtain. Also they were muchless uniformly broadcast to the nationwide population. The difficulty ofstudying the non-news messages would have made the opinion calculationsmuch less precise since accuracy depend on analysis of all theinformation available to the public.

Without doubt, there was significant local advertising and othercampaigning in Iowa in preparation for the caucuses and in New Hampshirein anticipation of the primary which occurred shortly thereafter.However, the poll series for this chapter was taken for the country aswhole. For the nationwide public, the national news news media probablywas reasonably representative of the information available.

The three previous issues shared the feature that most members of thepopulation probably were not personally affected by policy on defensespending, troops in Lebanon or the candidates in the Democratic primary.Although a small fraction of the population was likely to to have beendirectly involved for any one of these issues, the majority probablyfelt no special individual concern about these topics. Even for troopsin Lebanon, the number sent was only several hundred so most peoplewould not personally have known anyone in the marine contingent.

It seemed likely that the population should have been more malleable forsuch distant issues than others where most people have a personal stake(Everson, 1982). An example of the latter type issue was the economicclimate of the country where there was a steady increase in thosefeeling that the economy was improving and a parallel decrease in thosewith a downbeat mood during the period from March 1981 to March 1984.There was not much change in the percentage feeling that economicconditions were staying the same (Table B.4).

The economic issue of the relative importance of unemployment andinflation was similar to the problem of the economic climate in that thepublic had personal experiences or observations with both problems. Bothissues might have been influenced by factors outside the mass media inwhich case the model might not have been expected to work if only APmessages were used to represent the driving forces for change.

Polls for unemployment versus inflation (Table B.5) showed that thepublic seemed to be very aware of the problem, as was true for theeconomic climate, with less than 3% in the Not sure group. This topicwas also chosen because there were significant movements in publicopinion, with opinion focusing on the importance of unemployment havinga high of around 50% and a low of around 20% in the time period from1977 to 1980.

As a reference point for the other computations, it was useful to havean issue for which there was little opinion change. If the model isvalid then it should also predict opinion constancy when required. Thistest was performed for the issue of whether aid should have been sent tothe Contra guerrillas fighting the government of Nicaragua from 1983 to1986. This issue was similar to that of troops in Lebanon in beingconcerned with military involvement in a small foreign country. From theopinion standpoint, the major difference was the large opinionfluctuations for troops in Lebanon and the very small variations forContra aid (Table B.6). Parenthetically, the polls for Contra aid wereonly obtained from the Roper Center after the scoring for the relevantnews messages to insure that the scoring would not be biased byknowledge of poll results.

3.2 Relevant Persuasive Messages in the Associated Press

With the polls in hand, the next step was to collect the pertinentpersuasive messages. Although it would have been desirable to sampleboth the electronic and written press, it was difficult and tedious toobtain the unaltered contents of television and radio news.

For television, there were the Television News Index and Abstracts ofthe Vanderbilt Television News Archive. However, the summaries were verybrief with abstracters condensing news segments as long a few minutesinto a single phrase. Clearly, analyses based on these summaries wouldhave depended heavily on the abstracters.

The case was much better for the print media. Here, it was possible toretrieve an entire news item in virgin form from electronic data bases.These sources contain not only the full texts of stories from wireservices such as the AP and United Press International, but alsonewspapers such as the New York Times and the Washington Post, and newsmagazines such as Newsweek and Time.

Since AP stories are likely to be representative of the mass media ingeneral (Chapter 2), this book focuses on AP dispatches. In addition,messages were restricted to this wire service because the audience sizewas more uniform for news from a single source. It would have beendifficult to weight the relative audience sizes of items in the AP withthose, for example, from news magazines with more limited circulations.

Even for AP stories, the impact of a dispatch depended on whether it wasactually printed and, if so, whether the placement was prominent.Important information in this regard could have been obtained from the"news budget" the AP routinely sends its subscribers twice a day. Thesebudgets are summaries of important dispatches in preparation. Newspapereditors save space in upcoming editions based on these budgets. Later, adispatch corresponding to a budget item is sent carrying the designatorBJT in its heading region. These budget stories typically stand a verygood chance of appearing in a featured place. Unfortunately, the budgetdesignator was stripped from AP dispatches before entry into the Nexisdata base used for these studies. Therefore, there was no convenient wayto know the probable disposition of an AP dispatch so all AP dispatcheswere assumed to be equally important. In future studies, it may be moreuseful to use the VuText data base from which the BJT designator is notremoved.

Fortunately, it is only necessary that all AP dispatches on a givenpolled topic have the same prominence since comparisons were only madebetween stories on the same issue. There would have been no problem withall articles on one topic being buried in small items in obscure placesin the mass media while all stories on another were given featuredtreatment.

3.3 Retrievals from the Nexis Data Base

One attractive feature of using a full text data base like Nexis is theretrieval method since the search is not at the mercy of abstracters.Instead, the investigator chooses combinations of key words which arescanned through every word in all articles in the data base. Althoughthe basic elements of the search commands are simple, complex thoughtscan be expressed by combining groups of words (see Appendix B for thedetails of the retrievals for the five examples in this book).

All stories with the desired word combinations were identified even iftheir main topic was not on the polled issue. For instance, an articledwelling on poverty might have included a single statement that too muchwas being spent on defense given the magnitude of the economic problem.Such a dispatch would have been identified in the Nexis search forarticles on defense spending. The same article might well have beenmissed by human readers. Therefore, the pertinent dispatches identifiedby the Nexis search for this book may were probably more complete thanthose in previous reports using human coders.

The decision could have been made to study only those articlesconcentrating on the polled issue. However, a deliberate decision wasmade to include the stories in which the issue was only mentionedperipherally. This choice seemed more appropriate since the public atlarge was exposed to the mass media in a different way from trainedreaders specifically scoring particular issues. The bulk of thepopulation was probably not trying to draw critical judgements for mostissues and might not have noticed when one article began and anotherstopped. Therefore, for the general population, an isolated statementfavoring less defense spending might have had as much of an impact in anarticle on poverty as in a story on defense spending.

With the possibility that the public might assimilate information for anissue from a wide number of contexts, the decision was made to beinclusive rather than exclusive in identifying potentially relevant APdispatches. The culling of truly pertinent stories occurred at latersteps in the text analyses.

The result was that over a thousand relevant dispatches were found foreach of the issues in this book. It seemed both impractical andunnecessary to analyze all dispatches for all issues. For four of thesix issues, it was decided to retrieve only a few hundred dispatches atrandom for detailed study. Since the dispatches were all numbered inreverse chronological order from the most recent to the one furthestback in time, retrieval by random dispatch number should have yielded arepresentative sample. For unemployment versus inflation and Contra aid,almost all of the identified articles were retrieved. These morecomplete retrievals were made to test the possibility that a few hundreddispatches was not enough and that a larger sample was needed.

Although it was possible to retrieve the full texts of each of thechosen articles, the decision was made to restrict all retrievals totext within 50 words both before and after one of the key words used inthe original search. Careful reading of typical stories indicated thatthis condition led to the retention of essentially all relevant textwith large portions of irrelevant text being discarded (see sample textin Appendix C). One of the most frequent examples involved extracts anddescriptions of news conferences in which comments were made on avariety of totally unrelated topics.

For defense spending, the search was for AP dispatches with "defense" ora synonym being close to "spending" or a synonym (Appendix B). Since theaim was to find all possibly relevant dispatches, no commands restrictedthe search to American defense spending. Dispatches on non-Americandefense spending were eliminated at a later step.

Among those dispatches retrieved using the 50 word rule, the amount ofcollected text was equivalent to about 199 words of retrieved text perdispatch. Since the typical AP dispatch contained 400-900 words, onlysmall extracts were obtained from most dispatches, consistent with theidea that comments on defense spending were frequently in articlesdiscussing other topics. Text on other topics outside the 50 word limitwas discarded.

The initial set of retrievals was made for calculations from 1977 to1984. A second set of retrievals was made from 1981 to 1986 to study theease with which the computations could be extended beyond 1984. For thissecond study, a set of retrievals was also made for stories aboutdefense waste and fraud (Appendix B). These stories were also analyzedto see if they contributed significantly to opinion favoring lessdefense spending.

For the topic of American troops in Lebanon, the search was for articleswith words referring to Lebanon, America and troops. In contrast to thecase for defense spending, many dispatches on troops in Lebanon wereentirely about this topic and were retrieved in toto since about 420words were retrieved per dispatch. In fact, the scanning of a randomcollection of stories showed that many were devoted entirely to Lebanon.

In the Nexis data base search for the Democratic primary, there wereseven candidates. Therefore, the search was for all dispatches with thename of at least one of the candidates. Because the typical retrievalhad 310 words per dispatch, under the 400-900 words per typical article,many dispatches were not retrieved in their entirety.

For the economic issues, it was conceivable that personal experiencewould contribute substantially to opinion on the economic climate. Inpriniciple, ideodynamic calculations should have inluded infons fromthose infons. Obviously, those infons are very difficult to study.Therefore, the assumption was made that the mass media included most ofthe important persuasive messages. The validity of this approximationwould be tested by the accuracy of the opinion projections.

As usual, AP dispatches were considered to be representative of massmedia messages so the Nexis data base was searched for word pairsreferring to the economic climate. The retained dispatches typically had197 words per average story. This number was close to the 199 found fordefense spending and corresponded to less than half the text of atypical AP dispatch. Therefore, most of the retrieved text came fromdispatches in which the economic climate was only one among severaltopics discussed.

For unemployment versus inflation, AP dispatches representing mass mediamessages were obtained from the Nexis data base by searching fordispatches in which unemployment and inflation were close enough thatthere was likely to be nearby text comparing the two. This gaveapproximately 177 words per dispatch, a figure comparable to those fordefense spending and the economic climate. Therefore, for unemploymentversus inflation as well, most dispatches covered several topics withnone being discussed throughout.

The AP stories in the Nexis data base relevant to Contra aid wereidentified by looking for the word Contras in close proximity to a wordreferring to spending. This topic was also like all others except fortroops in Lebanon in having fewer retrieved words (258 words) peraverage dispatch than present in the typical AP story.

3.4 Summary of Data Used for Opinion Projections

There were only two types of data collected for the calculations in thisbook, time series of public opinion polls and AP dispatches relevant tothe polled issues. The issues were chosen in part because they were eachpolled extensively with 8-62 time points in each series. Aside from theissue of Contra aid, another consideration was that opinion shouldchange significantly. In addition, the topics were diverse, and most ofthe relevant persuasive messages were expected to be in the mass media.

Reasonable approximations (see next two chapters) to measured opinionpoll time trends could be computed using nothing mass media messages asrepresented by AP dispatches retrieved from the Nexis data base.

FIG. 3.1 Poll data for defense spending. After subtraction of the Don'tKnows and the Not Sures, the data in Table B.1 were normalized to 100%and plotted using the same symbols, one for each of the four independenttime series (see Appendix B).

CHAPTER 4 Computer Test Analysis by Method of Successive Filtrations

Since the methodology in this book was designed to project publicopinion from persuasive messages in Associated Press dispatches, twosteps were involved. The first was to score the AP dispatches in theformat of ideodynamics (Chapter 1 and Appendix A). The second was to usethe scores to project poll results.

The decision for this book was to score the messages using a computermethod. The advantages were two-fold. First, consistency was guaranteedso human Judges could not be accused of scoring, either consciously orsubconsciously, with one eye on the measured opinion time trends inorder to improve the projections. Second, the dictionaries and rules forcomputer analyses would shed light on those features of the text whichwere especially important for influencing public opinion.

4.1 General Text Analysis Programs

Early in the history of computer assisted text analysis, the strategywas to customize the analyses to individual problems (see e.g.McClelland's N- Achievement in Stone et al., 1966). In more recenttimes, the emphasis has been on the development of general text analysisprograms and dictionaries applicable to a wide variety of text. Thefirst widely used program of this latter sort was the General Inquirer(Stone et al., 1966) which has been updated (Kelly and Stone, 1975).

The General Inquirer and a number of relatives assign words in the textto a number of predefined categories (typically in the range of ahundred) using dictionaries and disambiguation rules with variousdegrees of complexity (see Weber, 1985 for a recent review). Thedisambiguation is necessary because many words in natural language cantake on different meanings in different contexts. A frequently citedexample is the word spring which can refer to a coil of metal, a time ofyear, a source of water, or a jumping action. Among recent dictionariesand complex disambiguation routines for the General Inquirer are theHarvard VI Psychosocial Dictionary (Dunphy et al., 1974; Kelly andStone, 1975), and the Lasswell Value Dictionary (Lasswell andNamenwirth, 1968; Namenwirth and Weber, 1984).

Text parsing Programs have also come from the natural language area ofartificial intelligence. Many of these are also general in nature,adaptable to diverse texts. Among these are ACTOR (Hewitt, 1976), BORIS(Dyer, 1982), HEARSAY II (Fennel and Lesser, 1977), MARGIE (Schank, etal., 1973), READER (Thibadeau, et al., 1982), DEREDEC (Lecomte, et al.,1984) and RELATUS (Duffy and Mallery, 1986).

Another general text analysis program based on the assignment of wordsto predefined categories is the MCCA program of McTavish and Pirro(1985). Like the programs mentioned above, MCCA is meant to be usefulfor a wide variety of text. In initial studies, the AP dispatchesretrieved for the studies in this book were analyzed using the MCCAprogram but the output did not correlate well with the sense of thedispatches in terms of supporting individual polled positions.

An examination of the output suggested several problems. One of the mostimmediate was due to assignment of individual words to predefinedcategories. As a result, relationships between words were lost. A primeexample was the use of negation words like not. After the program run,there was no way to determine what was modified by this word. This was aserious problem for wire service stories which could cover manydifferent topics in the same dispatch as happened in reports of pressconferences. In such stories, only a small portion of the dispatch wasrelevant to the issue under study. Therefore, the mere abundance ofnegation words was not highly indicative of whether the story had anegative connotation for the topic being examined.

A further problem was the predefined categories in the MCCA programincluding those for Good, Sin, Faith-Belief, etc. These categories couldgive a good idea of the psychological state of the message generator.However, the categories were not easily adaptable to policy questionssuch as whether American troops should be sent to Lebanon, especiallysince some of the most crucial words were highly unusual includingIsraeli, Syrian, British, French, Italian, Christian, Shiite, Druse, andothers. To be successful, a general text analysis program would haverequired an astronomical number of word categories to distinguishbetween these troop sources while simultaneously separating Americanpolicy in Lebanon from that in Grenada, where troops were also sent. Theunsatisfactory nature of preassigned categories was not restricted tothe MCCA program but was characteristic of all automated text analysesassigning words to all purpose categories.

Another important drawback was the multipurpose function of MCCA, andother general purpose programs. To be useful for a wide variety of text,as mentioned above, disambiguation procedures were needed to account forwords having different meanings in a wide variety of contexts.Therefore, the dictionaries tended to be relatively small with thedisambiguation subprograms concentrating on assigning proper meanings tocommon words. As dictionaries grow, the disambiguation problems increaseeven more rapidly.

The limited dictionaries frequently were inappropriate for textimportant to public opinion. Key words in such text were often highlyspecialized, including many proper nouns as just discussed. Furthermore,each individual issue was also likely to have words requiring customizedand unusual implications. For instance, "neglect" in the context ofdefense spending implied that the military budget should be increased.

The refined disambiguation subprograms in general text analysis programsalso meant that the dictionaries were very difficult to change because asingle addition or deletion meant that all disambiguation steps had tobe checked to see if any should be modified by the word change. Theabsence of disambiguation procedures would certainly have facilitateddictionary changes--however, at the cost of increased confusion in wordusage.

4.2 Strategy for Content Analysis Using Successive Filtrations

The previous considerations suggested that an improved method ofcomputer content analysis should: (1) be able to assure a high degree ofprecision by examining key words in proper relationships with eachother, (2) be able to resolve ambiguities in natural language, and (3)permit the use of flexible dictionaries including very specializedwords.

The strategy in this book was to abandon the idea of making a singlepass through the text using a general program and a fixed dictionary.Instead, there was return to the philosophy of tailoring the analysis toindividual issues. As noted by Weber (1985), "single-concept codingschemes often have high validity and reliability."

One of the important departures from single pass programs like thosediscussed in the previous Section was the decision to process the textthrough a series of "filtration" computer runs to remove differentirrelevant material at each step. Each filtration run was guided by asmall number of rules and a rather limited dictionary with only a fewkey words, words with few ambiguity problems in the context of theremaining text.

There were some unexpected advantages to this strategy of makingmultiple passes through the text. By doing so, it was possible for thedictionary and rules at each pass to be very simple thereby minimizingthe errors in their construction. As soon as one or two criteria wereidentified by reading a random sample of the text (typically 50,000 to100,000 characters), a small dictionary and set of rules could bedevised for eliminating irrelevant passages.

At the end of the run, the text was now more homogeneous than that inthe input file. This meant that it was much easier to decide on thedictionary and rules for the next filtration step. Greater texthomogeneity meant that the reader's mind was not cluttered with materialwhich as not pertinent.

However, as important, if not more so, was the fact that wordrelationships useful for the analysis were typically already built intoeach set of filtration rules. For example, the first filtration step fordispatches on defense spending restricted this spending to Americanpolicy (see Section 4.4 below). Therefore, this step already imposed therelationship that defense spending be linked to the United States. As aconsequence, there was no need to worry about this linkage in furthersteps in the analysis.

Another advantage of the filtration steps was the disambiguationaccomplished. As mentioned above, the word "neglect" typically impliedthe support of more military spending. Without the filtration steps tofocus on paragraphs specifically discussing defense spending, it wouldnot have been possible to give "neglect" this very special meaning.

Superficially, it might be supposed that multiple computer runs wouldgreatly slow the analysis. Interestingly, this was not the case.Although there was time lost in analyzing the same text several times,there was a compensating gain. First of all, as noted above, the stepsfor developing the dictionaries and rules were simple and hence rapid.

In addition, the machine time needed for each run was shortened becausethe dictionaries and rules were shorter than would have been needed forthe more complex dictionaries and rules needed for a single passanalysis. Therefore, a great deal of computational time was saved.Furthermore, with each succeeding pass, the amount of text diminisheddue to the elimination of irrelevant material so the runs became evenfaster. Given the slowness of checking long dictionaries and evaluatingcomplicated assignment rules, the total machine run time could beshorter with a strategy of multiple runs using simple dictionaries andrules.

After successive filtrations, the text became sufficiently homogeneousthat a simple dictionary and a small number of rules could beconstructed for assigning numerical scores for the extent to which eachAP dispatch favored the positions being considered. These scores werefor the infon contents discussed in Chapter 1.

4.3 Sketch of Filtration and Scoring Computer Program Runs

Although the text analytic steps--both for filtration and scoring--aredescribed in detail in Appendix C for each of the five cases studied, itis useful to outline the procedure here.

The analysis involved: (1) checking the text for dictionary words, (2)identifying clusters of key words, and (3) making filtration or scoringdecisions based on the word clusters.

To begin with the first step, a new dictionary was constructed for eachanalysis with words grouped under a limited number of concepts. Fordefense spending, for instance, one of the essential concepts was"American" since the topic was spending by the United States and notsome other country. The words under this thought did not actually haveto be a synonym for America. It was sufficient that there be a strongimplication of the United States. Therefore, acceptable words includednot only "America," "United States," and "U.S." but also "House,""Senate," "Ford," "Carter," and "Reagan."

After the dictionary check, the concepts corresponding to the dictionarywords were evaluated. In scoring defense spending, for instance, onefiltration steps was to select AP dispatches discussing the idea of"American defense spending." This idea required the simultaneouspresence in the article of the three concepts of: "American," "defense,"and "spending."

This example is the simplest case where the only requirement was thatthat certain concepts all be together in the same text. In more complexcases, it was necessary to insist that two words be close together tobelong to the same thought. For instance, a "defense" word had to beclose to a "spending" word in the filtration step to select paragraphsactually devoted to defense spending. Otherwise, the spending wasprobably for some other purpose.

In yet other cases, the sequence was also important. In the Presidentialprimary example, the word "endorsed" implied the concept of "favorableto candidates whose names are further into the text". In other words,"endorsed" was interpreted to favor a candidate only if it appearedbefore the candidate name as in "endorsed Mondale." There was no suchconnotation when the sequence was reversed as in "Mondale endorsed."

However, "endorsed" was sometimes also found in the context of "endorsedby." In this word pair, the word "endorsed" was combined with theconcept of "change the direction of action of the word" due to thepresence of the word "by." The result was the idea of "favorable tocandidates whose names are earlier in the text." In this way, the phrase"Mortdale endorsed by" was scored as supporting Mondale while "endorsedby Mondale" was not.

Similarly, it was not difficult to add an additional word like "not" ofthe negation class so that the phrase "Mondale was not endorsed by"could be considered be unfavorable to Mondale. Therefore, both the orderand distances between words could be used for the filtration or scoringdecisions.

After the words in the text were represented by the concept categoriesand after groups of concept categories were evaluated to describe morecomplex meanings, decisions were made about the text being considered.These decisions depended on whether the text was to be filtered orscored.

For filtration steps, certain Complex meanings led the text to beingdiscarded while others meant that the text was retained. For some runs,the decisions pertained to the entire retrieved text from a dispatchwhile, for others, decisions were made for individual paragraphs withina story. The program output from a filtration run was a shortened filecontaining only the non-discarded text which was then either refilteredor scored using another dictionary and set of rules.

For scoring runs, individual paragraphs were evaluated as favoring apolled position if there was a word cluster referring to support forthat position. For a paragraph with more than one scored cluster, thescore was divided among the corresponding positions. The dispatch scoresfor the scored positions were the sums of the individual paragraphscores. Because every scored paragraph added to the infon content score,a dispatch had a higher salience if it transmitted more relevantmaterial (Chapter 1).

The text analyses for the five cases in this book are sketched below.

4.4 Text Analyses for Defense Spending

Stories on this topic were processed through two successive filtrationsteps before by the scoring run. In Appendix C, one of the retrieveddispatches for defense spending is traced through all the steps toillustrate the method. That dispatch also demonstrated that text furtherthan 50 words from one of the search words (Chapter 3) was usuallyirrelevant to the polled topic.

The text analysis for defense spending began with a filtration stepusing the criterion that the dispatch concern American defense spending.Therefore, the requirement was that the dispatch have at least one wordeach referring to America, defense and spending.

In addition, entire dispatches were discarded if they had the words"fund" or "aid." Fund (with no trailing characters like funds, funding,etc.) was found in articles on topics like the Environmental DefenseFund. Aid (not aiding or aided) tended to refer to American aid fordefense spending in another country like El Salvador.

After this dispatch filtration step, the 692 dispatches were reduced to377 with 199 words per average dispatch.

After the previous Step removed entire dispatches irrelevant to Americandefense spending, a single criterion was used for the next filtrationstep. Only paragraphs directly concerned with defense spending wereretained. This was accomplished by requiring that a word referring todefense be close to a word connoting spending. Since the previousfiltration step had already enriched for dispatches on American defensespending there was no need to require a reference to the United Statesin this second filtration.

Some spending words did not directly refer to money. For instance, "cut"and "side" almost always implied funding and were therefore consideredto be spending words. The word "side" is perhaps a surprise, but it wasfound in phrases like "defense side" in budgetary discussions. Theability to use a multipurpose word like "side" in this very specificfashion was due to the disambiguation performed during both the initialNexis search (Chapter 3 and Appendix B) and the previous filtrationstep.

After the second filtration, all but 37 of the 377 stories retained fromthe previous filtration were found to contain pertinent paragraphs.However, for each dispatch, many paragraphs concerned other subjectssince the text within each article was reduced to 27% of the original.

After the previous two filtration steps, the remaining paragraphs weresufficiently homogeneous that the computer could easily score for infonsin favor of more, same and less defense spending. In this scoring, thefirst condition was the removal of all phrases referring to"non-defense."

Then, two alternative sets of dictionaries and rules were used. In one,the dispatches were scored for support for all three of the positions ofmore, same and less defense spending. In the alternative scheme, thescoring was only for support of more and less spending. The two scoringschemes differed in both their dictionaries and in their rules.

In the scoring for three positions, only one criterion was used to scorea paragraph as favoring more, same or less defense spending. Thatcriterion was that a defense word be in close proximity to modifierwords which favored one of the positions. Naturally, the orders anddistances between modifier words were used to fine tune the sense ofmodifier. Since the previous filtration step had already required thatdefense words be close to spending words, there was no need to examineboth spending and defense in the final scoring step. This step wasperformed on 5-10% of the words in the original retrieved text. Amongthe 377 stories truly relevant to defense spending, 72% had at least onescored paragraph. Therefore, the scoring was not based on only a smallnumber of dispatches.

The text scored for three positions as just described were rescored forthe support of only the two positions of favoring more or less spending.The details are given in Appendix C.

In the two position scoring, the rules were simplified by removing thecategory of infons favoring same defense spending. Thus information wasforced into two categories, that favoring more and that favoring lessspending. The result was that words like "keep" and "maintain" whichreferred clearly to same spending were deleted from the dictionary. Alsosome words like "freeze" were reassigned from supporting same spendingto favoring less spending.

Besides the changes dictated by decreasing the scoring categories, thedictionary was further altered. Some of the words favoring more(bolster) and less (alternate, weaken, without) were deleted. Also,nuclear arms reduction was interpreted to favor less defense spending.In the three position analysis described earlier, nuclear arms reductionhad no implications for defense spending. Thus the words "nuclear" and"arms" were only present in the dictionary for the two positionanalysis.

This inclusion of arms reduction meant that eight more dispatches werescored as favoring more or less defense spending. Therefore, the finalnumber of dispatches with at least one paragraph with a positive scorewas 280 or 74% of relevant 377.

Both the initial Nexis retrievals and the subsequent text analysesdescribed above focused on information directly relevant to defensespending. However, it was also possible that indirect messages couldinfluence opinion on this topic. A good candidate for such indirectpersuasive information was stories on defense waste and fraud.

Therefore, AP dispatches were identified if they had a word connotingdefense close to a word mentioning waste, fraud or corruption (AppendixB). There was no requirement that the story be about defense spending.Of the 878 identified from Jan. 1, 1977 to Apr. 12, 1986, 512 wereretrieved at random for text analysis.

The harvested text was passed through two filtration steps, the firstdiscarding entire dispatches if they were about waste and fraud incountries other than the United States. The second filtration retainedthose paragraphs with word combinations implying both defense and wasteor fraud or corruption.

At this point, every remaining paragraph was scored as favoring lessdefense spending if it contained a word cluster uniting the ideas ofdefense and waste or corruption or fraud. The implication was that suchstories would suggest that funds for the military were unwisely spentand should therefore be reduced. Of the original 512 retrieveddispatches, 147 or 29% were scored as having an average of 1.3paragraphs each on defense waste and fraud.

4.5 Text Analysis for Troops in Lebanon

The text for troops in Lebanon was the most complex of the fiveexamples. Nevertheless, only two filtration runs were needed before thefinal scoring step (Appendix C).

Since essentially none of the retrieved dispatches were totallyirrelevant to the topic, the first filtration focused on selectingrelevant paragraphs rather than eliminating entire dispatches. The maincriterion was that paragraphs should be directly concerned with Americantroops in Lebanon. Therefore, the retained paragraphs had to have wordsmentioning America, troops or policy, and Lebanon. In addition, backupconditions eliminated paragraphs on other topics like the domesticAmerican economy, or troops from other countries.

The rules for the Lebanon analysis permitted ideas to be carried forwardfrom one paragraph to the next so that pronouns like "they" and "them"referred to troops if troops were mentioned in an earlier paragraph.Similarly, the understanding was that a story continued to be aboutLebanon if that area of the world was mentioned previously unless therewas a change of locale to places like Grenada or the Caribbean since theUnited States also had troops in Grenada during part of the same timeperiod. In addition, a new Lebanon reference was needed when words likeArab, Christian, and Druse were found in the previous paragraph. Americawas also carried forward until mention of another country like Britain,Italy and France which were also part of the Multinational Forcestationed in Lebanon in 1983-1984.

While references to America and Lebanon continued to be carried forwarduntil a paragraph had a word to cancel the thought, the troop idea wasonly carried forward if the next paragraph actually had an appropriatepronoun.

In this first filtration step, the text on Lebanon was reduced to 31% ofthe original retrieval. However, the number of dispatches with at leastone relevant paragraph only dropped by 11%. As a result, the averagedispatch now had 156 instead of 420 words.

The second filtration step was influenced by the requirement ofretaining words like "keep" and "support" in the final scoringdictionary. These words were needed because the scoring was for thepositions of favoring more, same or less troops in Lebanon. However,such words were also plentiful in text describing military combat. Forinstance, sentences sometimes spoke of artillery support for groundgroups.

To avoid the confusion, it was decided to remove all text on actualcombat and all paragraphs on entertainer Bob Hope's Christmas visit toLebanon. The remaining paragraphs directly advocated positions on thedeployment of troops in Lebanon.

In this step, the implicit decision was that descriptions of combatcould be ignored. Such descriptions were not logically associated withthe need to send either more or less troops. If the combat situation wasunfavorable, the public could either feel that more troops were neededto correct the problem or that troops should be withdrawn because thesituation was hopeless.

This second filtration step reduced the text to 15% of the original. Thedispatches with at least one relevant paragraph only decreased to 80% ofthe original. The remaining text of story each averaged 85 words, quitesimilar to the 81 found after the two filtration steps for defensespending.

After the two filtration runs for troops in Lebanon, the text was scoredfor favoring more, same and less troops. The major criterion was toscore for a word referring to troops or policy near a modifier wordconnoting more, same or less. Some words--like stay and withdraw--wereable by themselves to favor keeping or removing troops since theconcepts of America and Lebanon were already present in all dispatches.The previous filtration step had assured that the remaining paragraphstook policy positions on American troops in Lebanon. Therefore, as fordefense spending, the previous filtration steps simplified the finalscoring.

Even after the filtrations, some words retained important ambiguities.One of the most notable was "peacekeeping." This word could refer to"peacekeeping force," in other words troops. Elsewhere in the text wouldbe phrases like "peacekeeping role," which tended to support a continuedtroop presence.

This problem was overcome by first asking if "peacekeeping" preceded"force" within a short distance. If so, then "peacekeeping" and "force"gave the concept of "troops." Otherwise, "force" was ignored because itwas used as a verb connoting coercion a significant amount of the time.If "peacekeeping" was not before "force," then it could act on a policyword to give the sense that troops should be kept in Lebanon.

A different strategy was used to assign the proper meaning to "state"(as in "he stated that"), a word with policy implications. However,"state" was also often found in the phrase "secretary of state."Therefore, the dictionary search was first for the pair of words "ofstate." Once this word pair was found and removed, "state" could referto policy.

Policy on troop deployment was frequently qualified by conditional wordslike "if, question, reappraise, why," etc. Since the majority of thedebate was between troop retention and troop removal, such a qualifierled to the paragraph have equal scores favoring the two positions.

Using these main conditions, the text after the two filtration steps wasscored for favoring the three positions of more, same and less troops.Of the original 467 dispatches, 54% had at least one paragraph with anon-zero score, again indicating that the scores were not based on asmall minority of the total dispatches.

4.6 Democratic Primary

From the poll data for the Democratic primary (Appendix B), people withopinions were divided into three groups, those favoring Mondale, Glennand Others. Therefore, the text was scored for these three groups ofcandidates.

Two different text analyses were used. One was based on what might becalled "bandwagon" words describing a candidates successes and failures.As noted by Brams and Riker (1972) and Straffin (1977), it is possibleto model the bandwagon effect mathematically assuming that uncommittedpersons recognize the winning side and adopt the traits of that sidewhile shunning the losers. However, the public must somehow assess whichsides are winning and losing. For political candidates, the analysis inthis book assumes that these perceptions are formed from descriptions inthe press of the candidates' successes and failures. The bandwagoneffect implicitly assumes that the decision to support a candidate ismade principally from these perceptions rather than on a thoughtfulanalysis of other pertinent factors such as a candidate's stand onissues. Therefore, the bandwagon words used in this book just refer tocandidates' successes and failures.

The alternative text analysis used candidate name counts and waspredicated on the idea that name recognition, regardless of context, wasthe crucial consideration for a candidate's popularity (Kinder andSears, 1985; Mueller, 1970a; Stang, 1974; Zajonc, 1968, 1980).

The more complicated analysis was obviously the one dependent onbandwagon words. Here, unlike defense spending and troops in Lebanon,the positions did not lie on a continuous scale ranging from one extremeto another. Instead, information for each candidate could be placed onsome scale from very favorable to very unfavorable. Rather than choosinga very finely graded scale, the text was simply judged to favor ordisadvantage a candidate.

In the bandwagon analysis, the first step was a filtration to select forparagraphs containing at least one of the last names of the Democraticcandidates. This step reduced the text to 55% of the original. All thedispatches still had at least one paragraph with a candidate name. Afterthis reduction, 199 words were left in the average dispatch.

After this filtration, paragraphs were scored for favoring anddisadvantaging all three groups of candidates. The main criterion wasthat a candidate's name should appear near an advantageous ordisadvantageous cluster of modifier words. These were bandwagon wordclusters in that no attention was paid to the reasons for an opinion ona candidate. The implication was that the public favored a candidate ifit looked as if that candidate was getting support from some sourcewhatever the reason.

The most common of these bandwagon words in the retained text were"elected"--found 112 times, "backed"--found 94 times, and"favored"--found 73 times. There were also negative words of which"failed"--found 24 times, "weak"--found 16 times, and"vulnerable"--found 11 times were among the most frequent.

This bandwagon scoring approach ignored the positions and activities ofthe candidates. For instance, Jesse Jackson was frequently in the newsduring the period studied because he was negotiating the release of anavy pilot shot down during a raid over Lebanon. Also, there werereports of Alan Cranston supporting the idea of a nuclear freeze. Theseand other reports on the candidates' activities and stands on issueswere consciously omitted during the bandwagon analysis.

Since candidate activities and positions were not scored, a majority ofthe articles had zero scores favoring or disadvantaging the candidates.Nevertheless, 37% of the original did have positive scores for at leastone of the six possible positions.

As an alternative to the bandwagon text analysis described above, it waspossible to imagine that candidate name use was sufficient to determinepopularity (Kinder and Sears, 1985; Mueller, 1970a; Stang, 1974; Zajonc,1968, 1980). Therefore, the same dispatches were analyzed for theirmentions of candidate names.

Paragraphs were scored as mentioning Mondale, Glenn or others sincedistinctions were not made for the contexts in which names werementioned. With all names being counted and no context being scored, all425 dispatches had at lease one paragraph in support of a candidate.

4.7 Text Analysis for the Economic Climate

The text analysis for this topic was performed in three steps. The firstwas to keep only those stories on the United States, the second was todiscard paragraphs which were not about the economy, and the third wasto assign the numerical scores. Sixty-six percent of the originaldispatches had at least one paragraph scored as supporting one or moreof the three positions with many dispatches having mixed messages.

4.8 Text Analysis for Unemployment versus Inflation

For the text analysis for this topic, there was only one filtration stepwhere dispatches on non-American economies were eliminated. Then theparagraphs were scored for the relative importance of unemployment andinflation using the criterion that words for these concepts should beclose to modifier words suggesting their importance. Forty-four percentof the original dispatches had at least one paragraph taking a positionon this issue.

4.9 Text Analysis for Contra Aid

The text analysis for this topic was developed without knowing theresults of public opinion polls. At the beginning of the analysis, theRoper Center was contacted at the University of Connecticut to determinethat a sufficient number of polls existed to obtain a reasonable timeseries. This was to assure that the text analysis could not beinfluenced by the results of opinion polls.

This topic also tested the ability of persons besides the author (Fan)to construct the dictionaries and rules for the text analysis. Workingas a team, three graduate research assistants (Swim et al., see AppendixC) from the Department of Psychology at the University of Minnesotaconstructed the dictionaries and rules for both the filtration andscoring steps.

Swim et al. first devised a filtration step to keep only thoseparagraphs with words connoting the United States, aid, and Nicaragua.The reference to the United States was required because the polls werefor American opinion on American aid to the Contras. At this step, notattempt was made to separate aid to the Contras opposing the Nicaraguangovernment from aid to the government itself.

After this filtration, Swim et al. decided that the text was ready forfinal scoring. Since the scoring is typically the most subtle step,rules and dictionaries were developed independently both by Fan and Swimet al. The text surviving the filtration was then analyzed twice, onceusing the procedure of Fan and once using the method of Swim et al.

Fan used the same general strategy employed for the other analyses ofthis book. He insisted that word combinations favoring or opposing aidbe in close juxtaposition with words referring to the Contras or asynonym. The result was paragraph scores either favoring or opposingaid, or both. The concept of supporting both positions could either bedue to two word clusters, one opposing and one favoring aid, or toconditional words like "if" appearing in paragraphs with only one wordcombination favoring either idea.

Unlike Fan, Swim et al. counted many word clusters only indirectlyfavoring or opposing aid. For example, "talks" . . . "failed" wasinterpreted to imply that aid should be given. In another instance,"Schultz" . . . "over eager" was interpreted to mean that no aid shouldbe given. Furthermore, Swim et al. did not impose the requirement thatany of these words be close to a mention of the Contras, as Fan haddone. Swim et al. took advantage of the filtration having restricted theparagraphs to those already discussing America, aid and Nicaragua.

By counting many indirect statements while Fan did not, Swim et al.found that 906 of the 969 retrieved dispatches had paragraphs relevantto Contra aid. In contrast, the number for Fan was 770. Therefore, Fandid not score 15% of the stories which Swim et al. found to be relevant.These stories presumably only had indirect statements on Contra aid. Inaddition, Swim et al. found 3.4 paragraphs per average scored dispatchto be relevant to Contra aid while Fan only found 2.0. stories.Therefore, by using a different dictionary and set of rules including asignificant number of indirect statements, Swim et al. gave scores toalmost twice as many paragraphs as Fan. Fortunately, both text analysesgave essentially the same opinion Projections (Chapter 5). This resultwill be discussed further in the Chapter 6.

4.10 Summary Features of Text Analysis by Successive Filtrations

One of the obvious strengths of the text analysis in this book was thateach issue was probed with a customized dictionary and set of rules.Therefore, it was possible to have much greater specificity andflexibility than was possible with general programs using fixeddictionaries and rules.

The necessity of different criteria was clear from the poor overlap inthe dictionaries and rules for the five examples in this book. Fortroops in Lebanon, proper nouns like Lebanon, Beirut, Christian andShiite were critical. For the Democratic primary another set of propernouns, those of the Presidential candidates, were crucial instead. Amongthe few words appearing consistently in all analyses were thosereferring to the United States and those words like "no" and "not"connoting negation.

The specificity of the analysis also meant that a single story could berelevant to a number of different issues. Thus the same dispatch couldbe analyzed with two different sets of dictionaries and rules in orderto provide two different types of infon scores. For example, some of thearticles identified as relevant to the economic climate were probablyalso pertinent to defense spending and/or unemployment versus inflation.Those dispatches would have yielded different types of scores dependingon the issue being studied. The same article could have had paragraphsboth favoring more defense spending and supporting an improving economicclimate.

Another advantage of the method of successive filtrations is itsgenerality. It may seem contradictory to speak of the generality of themethod when the advantage discussed in the previous section wasspecificity. However, the method was general due to the overall strategyof progressively culling irrelevant text in a series of filtrationsteps.

In addition, the text analysis of this book benefitted from the use ofsimple dictionaries and rules. This was possible using two sequentialfiltration steps when two distinct criteria could be developed to removeirrelevant text. The resulting simplifications in the rules minimizedlogical errors in their construction.

The simplifications were possible due to the ability of filtration stepsto relate thoughts within the text to each other. For defense spending,for instance, the first filtration imposed the condition that alldispatches be about the United States. Therefore, in subsequent steps,the concept of America was already implicit in the remaining text. Therewas no need to include a reference to the United States in the secondfiltration step selecting for paragraphs directly speaking of defensespending. In consequence, words referring to the U.S. could have beenanywhere in the text in the first filtration and to be totally absent inthe second. A single set of rules for a one pass program would have beenmuch more complex than the two individual sets of rules combined. Therules for a one pass run would have had to have been able to retainparagraphs on defense spending when there was also reference to Americaeither earlier or later in the dispatch.

The successive text filtrations also permitted the use of words withunusual meanings (see above). Very general words (like "cut" and "side")could safely be used in special senses (like spending) if previousfiltration steps put such code words into the proper contexts. Also, thespecific, unusual and jargon-like nature of many words was revealed onlyas the text became more homogeneous. Therefore the text filtration wasvery useful for word disambiguation.

The keys to handling code words were: (1) the strategy of textfiltration before final scoring, and (2) the tailoring of the rules anddictionaries to individual situations. The use of code words was muchmore problematical for general text analysis programs where the sameprogram and dictionary was used for a wide variety of text.

Contrary to what might be expected, the time required to analyze thetext was decreased rather than increased by the filtration steps eventhough some portions of text were reanalyzed several times. This economyoccurred at three steps. The first was in the construction of thedictionaries and rules.

Typically after examination of 50,000-100,000 characters of text fromrandom dispatches, sufficient repetition was seen that most of therelevant rules and dictionary words could be extracted.

The time needed for obtaining a workable dictionary and rules wasshortened by using only one or two criteria for each filtration run.After the filtration, the text was more homogeneous and more dispatcheswere represented for a given number of characters of text sincesignificant amounts of irrelevant text were discarded. Therefore, thetext for constructing the dictionaries and rules was derived from moreand more dispatches as the files were filtered. This had the advantagethat the most subtle decisions, those used for the scoring, were madefrom large samples of relevant text, more than was practical with theunfiltered stories where the density of pertinent material was low. Itwas obviously more time efficient to make rules from text highlypurified for relevance. Also, the fewer the rules and the smaller thedictionaries, the less was the time needed to debug the logical errorsin the rules.

The strategy of using simple dictionaries and rules also resulted inshortening the computer runs themselves. The initial filtrations usuallyemployed the simplest rules and the smallest dictionaries so the runswere quite rapid. With successive filtrations, the amount of nextdiminished so that more complex rules and larger dictionaries did notlead to significant increases in time. Using a one pass computerprogram, the rules would have been more complex than those used at anyone step and the dictionaries would have been bigger, thereby slowingthe runs. In other words, a great deal of computer time would have beenspent carefully examining irrelevant text.

The strategy in this book was to tailor each analysis, at all steps, tothe specific question being asked. Consequently, no additional time wasrequired to interpret the outputs of the computer runs. As described inthis chapter, the final scoring runs gave the infon content scores interms of the number of paragraphs supporting individual positions. Thesescores were then used directly for calculating public opinion withoutfurther interpretation.

The situation is quite different for analyses using predefineddictionaries and word categories in programs such as those discussed inSection 4.1 above. For those programs, no time was spent in theconstruction of the dictionaries and rules. Instead, the entire time wasused in the interpretation of the data generated and that time could beconsiderable. Sometimes, as for the examples in this book, there waseven no obvious way to interpret the output of a general text analysisprogram like MCCA in a useful form.

The methods in this book were also designed so that the logic of everystep was directly related to the initial goal of each analysis. Thecustomized nature of each step meant that it was possible to make thepositions much more specific than was possible using the predefinedcategories in general text analysis programs.

However, even for such general programs, it is sometimes possible toextract themes not present in individual predefined dictionaries byperforming factor analyses based on separate runs using differentdictionaries. For example, Weber (1985) used this technique to show thateditorials with frequent reference to American leaders and topics willalso have a high content of words relating to high status andoccupations. Although factor analysis is a powerful procedure, it isdifficult to consider its greater indirectness to be an advantage.

Another important consideration in developing the methods was to obtaina system where it was very difficult to fit the text analysis to polldata, either consciously or unconsiously. There is no easy way to adjustthe text analysis with the goal of matching opinion percentages.Adjustments in the content analysis would have involved changing therules or the dictionaries. However, any such changes would have affectedthe analysis for all AP dispatches. Therefore, there was no good way offoreseeing the effects on the opinion projections.

Any changes in the dictionaries and rules for the text analysis also hadto be Justifiable as logical consequences of the meanings within thetext. There was no place in the programs to add arbitrary correctionfactors.

Also, the dispatches were examined by design in random order during theconstructions of the dictionaries and rules. Therefore, it was neverobvious which dispatch scores needed to be changed in order to get abetter opinion projection. In fact, for no analyses were opinion polldata examined during text analysis steps. Therefore, the devising of thedictionaries and rules proceeded without regard to poll data for allanalyses even though it was only for the Contra aid example that thosedata were not available until the analysis was finished. Furthermore,the dispatch scores went through additional mathematical manipulationsbefore the final opinion projections were obtained (Chapter 5). It wasvery difficult to guess the final shapes for the opinion projectioncurves from the raw infon content scores. The quantitative predictionsrequired calculations using the projection computer program.

All these considerations meant that the text analysis had to beperformed independently of the opinion projections based upon them.

4.11 Extensions of the Text Analysis Procedure

The computer content analysis in this book should be applicable to textother than those used for calculating public opinion. For instance,responses to open ended questionnaires can be examined for theircomments on different topics. Employee letter of recommendation might bescored quantitatively for support of different traits desired for a job.

Different analyses would require different dictionaries and rules. Butthe overall strategy is broadly applicable using the general textanalysis procedure described above.

The strategy in this chapter has required that all the dictionaries andrules be entered by the investigator. There are no provisions formachine refinement. However, as more experience is gained on the sort ofrules which are admissible and the types of words which fit into variousclasses, software might be written which will permit the computer to aidin the development of the dictionaries and rules. The machine mightinitially be assigned tasks such as looking for similar words to add toa dictionary. Another computer function might be to check forconsistency whenever new words and rules are added to a preexistingdictionary and accompanying set of rules.

Later, as the guidelines for the dictionary and rule constructionsbecome clearer, these guidelines might be included in computer programsto suggest both new rules and dictionary words. As more and more suchguidelines are included, the procedure might be gradually converted froma fully manual system to one with ever greater degrees of automation indictionary and rule development.

CHAPTER 5 Projections of Public Opinion

The first chapter in this book outlined ideodynamics and showed how thismathematical model could be used to calculate public opinion frompersuasive messages available to the population. The third chapterproceeded to describe six sets of data used for the computations. Thosedata were time series of public opinion polls and Associated Pressdispatches relevant to the polled issues. The fourth chapter thenpresented a new computer method for scoring AP dispatches for theirsupport of various polled positions.

The present chapter details the use of these scores to computepercentage values for public opinion throughout the time interval forwhich poll data were available. Opinion is calculated for all six of thecases examined . Since many of the examples point to the same generalconclusions, the ramifications of the individual studies are notdiscussed in this chapter but, instead, are explored together inChapters 6 and 7.

As outlined in Chapter 1 and discussed further in Appendix D, theopinion calculations involved three steps: (1) conversion of the APdispatch scores into persuasive force functions appropriate for thecomputations, (2) construction of detailed population conversion modelsfor the effect of persuasive forces on various subpopulations, and (3)actual calculations of expected poll results. These steps are nowconsidered in detail for the six issues analyzed in this book.

5.1 Opinion Predictions for Defense Spending

Chapter 2 showed how AP stories were collected for defense spending andChapter 3 then described the methods for obtaining numerical scores foreach of the stories. Two sets of scores were obtained for each story.For one set, the dispatches were scored for their support of the threepositions of more, same and less spending. For the other set, thestories were scored for their support of only the two positions of moreand less spending.

Both sets of scores were then used to compute persuasive force functionsdescribing the time trends of the persuasive forces favoring each of thescored positions. For every position, a separate persuasive forcefunction was computed. When there were three sets of scores, there werethree functions favoring the same three positions of more, same and lessspending (FIG. 5.1). Similarly, two sets of scores yielded two functions(FIG. 5.2). To compute these functions, a curve like that described inFIG. 1.1 (top frame, Chapter 1) was computed for each infon. Then allthe functions for individual infons favoring the same position wereadded together as shown in FIG. 1.1 (bottom frame). The additivity meantthat opinion reinforcement and information saturation were ignored(Chapter 1). When the resulting functions were plotted (FIGS. 5.1 and5.2), the steep rises followed by the gradual declines in FIG. 1.1 werecompressed into spikes due to the condensed time scale of 7 years. Theseparate sets of persuasive force functions belonging to the two sets ofinfon scores were used independently for opinion calculations.

For both sets of functions, it was assumed that AP dispatches couldrepresent all the relevant information available to the the public, thatall scores had the same weights, that the score for a position onlycontributed to the persuasive force for that position, and that thepersistence constant had a one day half-life (see below). The majordifference between the two sets of plots was the deletion of infonsfavoring same spending (FIG. 5.2) when that position was not scored.Most of those infons were partitioned between the upper and lower curvesof FIG. 5.1.

To use the persuasive force functions, it was necessary to devise apopulation conversion model. This was already done for the defensespending analysis (FIG. 1.2). When used for infons scored for more, sameand less spending, all three persuasive forces were used in thecalculations. When the same model was applied to content scores forinfons favoring only more and less spending, the persuasive forcefunction favoring same spending was always zero.

The ideodynamic equations corresponding to the population conversionmodel were then written (Chapter 1 and Appendix A). To use theseequations, it was first necessary to set their parameters: thepersistence half-life, the modified persuasibility constant and anyrequired refining weights. In initial trials, the refining weights wereall set to 1.0 corresponding to the approximation that all infon contentscores had the same weight. This was a safe strategy because therefining weights usually differed very little from 1.0.

To set the other constants, opinion time trends were calculated usingarbitrary values for the persistence constant and the modifiedpersuasibility constant. At each time corresponding to that of an actualpoll, deviations were computed between calculated opinions and theactual values starting with the measured opinions at the time of thefirst poll (Chapter 1 and Appendix A, Equation A.14). The squares ofthese deviations were calculated for all poll values, and averaged togive the Mean Squared Deviation (MSD) (Appendix A). The chosenpersistence and modified persuasibility constants were those giving theminimum MSD.

Refining weights different from 1.0 were only tested if the predictionswere systematically high or low for one or more of the opinionpositions. If a refining weight gave a significant improvement in theMSD, then the persistence and modified persuasibility constants werereoptimized for the new refining weight(s). The final constants in thischapter were those giving the least MSD for all constants, unlessotherwise stated.

Rather than simply computing the MSD for every set of trial constants,time trends of opinion projections resulting from a number of arbitraryvalues for the constants were typically plotted to examine qualitativelythe consequences of increasing or decreasing particular constants. Basedon these plots, it was clear that persistence half-lives much longerthan a day usually meant that the population would have responded tomedia information more slowly than was actually found. Also, as themodified persuasibility constant increased, the fluctuation in opinioncalculations became larger and larger around a general time trend. Thisresult was expected since a larger persuasibility constant correspondsto more volatile issues with more people being persuaded for the sameamount of information.

Given these qualitative observations, systematic trials for thepersistence constant typically started with a one day half-life. Then,additional values were tested, increasing iteratively by factors of twountil MSD 5 to 10 times the MSD at the one day half-life were reached.Test values for the persuasibility constant usually began with valuesvery close to zero (e.g. 0.001 per AP paragraph per day) correspondingto a population being impervious to persuasion and then increased intwo-fold steps beyond the value for which the minimum MSD was reached.In the region where different parameter values gave approximately thesame MSD, values were tested on a finer linear scale between thetwo-fold jumps.

For all parameters, the MSD were plotted against trial values of theparameters (see optimization curves below). For these plots, the valuesfor the other parameters where those giving the minimum MSD. In thisway, the reader can assess the sensitivity of the MSD to changes in thetrial parameters. Where the optimization curves are steep, in the rangeof a minimum, the MSD is very sensitive to parameter changes. Where thecurves are shallow, wide variations in the parameters will haverelatively small effects on the MSD. It is possible to read from theoptimization curves the amount of permissible variation in a parameterbefore the MSD increased beyond the tolerance limits set by the analyst.The values discussed in the remainder of the chapter will refer to thevalues of the parameters at the minima of the optimization curves.

Turning away from general strategy and toward the specifics of theprojections using infons scored for more, same and less spending, it wasfound that the optimal modified persuasibility constant was 0.6 per APparagraph per day (see below). Expected opinion throughout the timeperiod of message collection was calculated using this optimizedconstant, the initial poll values and the persuasive force curves inFIG. 5.1 computed using the best value for the persistence half-life,one day (Appendix A, Equations A.26 and A.29). The expected opinion isplotted together with actual poll data in FIG. 5.3.

The one day value for the optimal persistence half-life was found byplotting trial values for the half-life versus the MSD (FIG. 5.4, lowerframe). This optimization plot shows that the MSD was still decreasingas the half-life was shortened to one day. It was conceivable that aneven shorter half-life would have been appropriate. However, it seemedunreasonable to set the half-life much shorter than one day since therewas at least that much ambiguity in the timing of the poll points and inthe timing of the AP stories dispatches.

The optimization curve for the persistence constant (FIG. 5.4, lowerframe), however, did show a second minimum over 100 days before a rapidincrease above that time. The precise explanations for the steep rise atlong half-lives are not clear.

The upper frame of FIG. 5.4 shows the optimization for the modifiedpersuasibility constant. This constant clearly gives a single, wellbehaved minimum MSD at 0.6 per AP paragraph per day. Comparison of theprojections in FIG. 5.3 with actual poll points did not indicate thatscores favoring any position were either systematically too high or toolow. Therefore, all refining weights were left at the value of 1.0 usedfor optimizing the persistence and modified persuasibility constants.These values meant that all infons were given the same weight.

For the projections of FIG. 5.3, only 692 of the 9314 identifieddispatches were studied. It was conceivable that smaller samples couldgive estimates which were just as good. Therefore, the 692 stores weredivided into approximately two equal, random subgroups of 325 and 383dispatches each. Only 16 dispatches in one group were also present inthe other. Using the modified persuasibility constant optimized above,opinions were recalculated using the two dispatch subsets (FIGS. 5.5 and5.6). Not surprisingly, there were greater deviations between thepredictions and the poll results with the smaller sample sizes. Thesedifferences could be seen quantitatively by the increase from 7.2 pollpercent for the total dispatch set to 9.4% and 10.3% for the two subsets(Table 5.1).

Yet another projection was made to explore what would have happened to asubpopulation comprised only of those favoring more defense spending.Therefore, the full set of infons and the optimized modifiedpersuasibility constant and uniform weights were used to remake theprojections assuming that the initial population only had peoplesupporting more spending (FIG. 5.7). The calculation showed that bythree years later the subpopulation should have behaved much as thepopulation as a whole.

This result was significant technically because it meant that there wasno need to account for the statistical errors inherent to the first pollpoint. The calculations ultimately homed to the values dictated by theinformation structure. The calculations for late times were notadversely affected even by extremely inaccurate initial conditions (FIG.5.7). If the errors in the initial poll point are not large, then theachievement of the proper values would have occurred much more rapidly.

So far, the discussion has concerned opinion calculations made frominfons scored for the three positions of more same and less defensespending. In addition, projections were also made for infons scored onlyfor more and less spending. For these projections the data were from all692 dispatches (FIG. 5.8). The same modified persuasibility constant of0.6 poll percent per AP paragraph per day was used for all infons sothat the projections could be compared directly. Again, all scores weregiven the same weight. The RMSD was 8.3 poll percent for the twoposition model in contrast to the 7.2% for the three position model.Therefore, the two position scores gave slightly less good results thanthe three positions scores.

In the calculations from both the two and three position scores, theprojected time trends of public opinion appeared to move in steps sincethe time between infons was usually large relative to the week duringwhich an infon had its effect. This was reasonable since only 7% of thetotal identified dispatches were studied. On an expanded scale, eachstep would have had the shapes in FIG. 1.3. The steps were also muchless tall because the modified persuasibility constant had a value over3000 times smaller in FIGS. 5.3 and 5.5-5.8, than in FIG. 1.3.

Using infons scored for either two (FIG. 5.8) or three (FIG. 5.3)positions, the time courses for all three opinions favoring more, sameand less spending followed quite well the main features of the actualpoll data. The change was most dramatic for people favoring morespending. There was the dramatic rise in opinion from 1979-1980 and theequally impressive drop from 1981-1982. Both the timing and themagnitudes of the actual changes were mirrored in the calculatedopinion.

Comparison of the poll projections (FIGS. 5.3, 5.5, 5.6, and 5.8) withthe infon force curves (FIG. 5.1 and 5.2) showed that the rise inopinion favoring more defense spending in 1979 was due to the greatincrease in information favoring this position. During this time,however, there was no diminution in messages arguing for less spending.The subsequent drop in support for more spending was not due to thedisappearance of messages favoring this idea. Rather there was asignificant augmentation in messages from the opponents.

Besides permitting the calculation of the best modified persuasibiltyconstant, the optimization curve for this unknown (FIG. 5.4, upperframe) also shows that the projected values are much better than wouldhave been predicted by the model that opinion had stayed constantthroughout the polling period. The condition of no opinion change isequivalent to a very small value for the modified persuasibilityconstant. When this constant is zero, the population is completelyresistant to information and will never undergo opinion changeregardless of the presence of persuasive messages. Therefore, a very lowmodified persuasibility constant such as 0.001 per AP paragraph per day(FIG. 5.4) gave three almost unchanging opinion curves throughout theseven year period, flat plots corresponding to the curves in FIG. 5.3.From the optimization curve of FIG. 5.4, the MSD was over 250 pollpercent squared for k'₂ =0.001. The corresponding value of around 50poll percent squared for the projection using the best persuasibilityconstant was less than 1/5 as large. This decrease in the MSD meant thatthe ideodynamic fit was much better than the model of no opinion change.

For comparison, 1000 simulations were made for predicted poll valuesdrawn at random. For each simulation, the MSD was calculated for thedifferences between actual poll values and random poll points. Theprobability that the ideodynamic predictions were no better than chancewere ascertained by counting the number of simulations among the 1000where the MSD from random poll results was smaller than that from theideodynamic predictions (right hand column of Table 5.1).

For completeness, square roots for the MSD (abbreviated as RMSD) werecomputed both for the random and ideodynamic estimates. The averagevalue among the 1000 simulations is also given in Table 5.1 (secondcolumn).

Because there were 1000 independent draws, it was also possible tocompute a standard deviation for the MSD, and from this standarddeviation, the number of standard deviations from the random MSD to theideodynamic estimate (Table 5.1, third column).

The chances that the calculated fit was no better than that obtainedfrom random poll points were less than 0.001 (Table 5.1). Therefore, theideodynamic projection is statistically much better than those obtainedby either no opinion change or by a random choice of poll values.

It should be noted that both poll points and projected opinion valueswere correlated with each other as time proceeded since opinion at alater time was dependent to some extent on opinion at earlier times.Therefore, in the absence of simplifying approximations which aredifficult to justify rigorously, it was inappropriate to calculate r²regressions requiring time independence for the poll values.

Ideodynamics postulates that the parameters in the opinion projectionequations should be constant, changing very little over the time periodof the calculations. If this is true and if language usage remained thesame, then it should have been possible to extend the opinionprojections to a later date simply by retrieving more AP dispatches andrunning the same programs under the conditions used for the studies from1977 to 1984. To test this hypothesis, the Nexis data base was searchedfrom Jan. 1, 1981 to Apr. 12, 1986 using the commands first employed fordefense spending. From the 10,451 dispatches identified, 1067 wereretrieved at random and analyzed using the same text analysis describedabove for the three positions favoring more, same and less spending.

The only change was a single alteration in the dictionary for the firstfiltration step. After 1984, the disease of acquired immune deficiencysyndrome became much more prominent. Therefore, its synonym AIDS wasfound in a significant number of dispatches describing spending fordefense against AIDS. Consequently, it was necessary to eliminatedispatches if they contained the word AIDS so this word was added to thedictionary in the first filtration together with the words "fund" and"aid." When any of these words appeared in a dispatch, the story waseliminated as being irrelevant to American military spending.

After the text analysis, 507 stories had non-zero scores for defensespending. This was about half the initial number of dispatches and wasnot very different from the 39% found in the analysis for stories from1977-1984. Perhaps the 39% was a little low since text ceased to becollected whenever the reader felt that a story was unlikely to be aboutdefense spending. Therefore, late mentions of defense spending wouldhave been discarded in the first set of retrievals. This manualinterference in the collection did not occur for the retrievals from1981 to 1986.

Since neither set of retrievals included all possible stories, and sincethere was overlap between the two data sets, all paragraph scores werecorrected to the expected value corresponding to all dispatches beingcollected. For instance, if only 1/10 of all dispatches were collectedat random in a time period, all paragraph scores in that period wouldhave been multiplied by 10. The persuasive force curves including thesecorrections are shown in FIG. 5.9. These data showed that there was verylittle change in the information structure from 1982 to 1986 with theratios of information favoring the three positions staying relativelyconstant.

Based on these results, it was expected that opinion would also be quitestable. This was indeed the case both for projected and measured opinion(FIG. 5.10). Stability in the poll results could be seen even thoughthere were fluctuations in the data from different polling organizationsafter 1982.

Despite this scatter, it seemed plausible that opinion favoring morespending might have been systematically overestimated after 1983.Therefore, a least squares optimization was performed over the entiretime period from 1977 to 1986 to see if a better projection could beobtained by giving information favoring less spending a greater weight.From the optimization curve in FIG. 5.11, a weight of about 1.2 gave amarginally better fit to the poll points. Given the small effect of theweight (decrease in MSD of less than 1/20), none was used in thecomputations of FIG. 5.10.

One interesting result of this optimization is that the smallimprovement in the fit required a greater weight for data favoring lessspending. That meant that information opposing more defense spending wasmore effective than information supporting more spending. Since manypro-spending infons came from the Reagan administration, the suggestionfrom FIGS. 5.10 and 5.11 was that a popular president and hisadministration was in fact less effective than the opposition in swayingpublic opinion, a result different from that obtained by Page andShapiro (1984) and Page, et al. (1987).

One possible explanation for the underestimate in opinion favoring lessdefense spending was the presence of additional indirect information notincluded in the analyses described above. A good candidate seemed to bestories on waste, fraud and corruption by defense contractors.Therefore, additional dispatches were retrieved focusing on these topicsand all paragraphs discussing these issues were scored as favoring lessdefense spending (Chapter 4). All these paragraphs were given the sameweight as paragraphs directly supporting less defense spending and apersuasive force curve based on these waste and fraud infons alone wasconstructed using the usual one day half-life (FIG. 5.12, top frame).Inclusion of these extra infons in the opinion computations meant thattheir persuasive force curves were added to the persuasive force curvefor infons directly advocating less spending (FIG. 5.9, bottom frame).Quick inspection shows that the waste and fraud infons (FIG. 5.12, topframe) were negligible when compared to the direct infons favoring lessspending. Therefore, there was very little difference between thepersuasive force curves with (FIG. 5.12, center frame) and without (FIG.5.9, bottom frame) the added infons.

This sameness in persuasive force meant that there was very littledifference between the opinion projections for more spending with andwithout considering waste and fraud (two lines in bottom frame, FIG.5.12). Therefore, by taking into account all relevant information, anideodynamic analysis was able to suggest that opinion on defensespending was not greatly influenced by information on waste and fraud.The only caveat to this interpretation was that the public might haveweighted this information much more heavily than information directlyspeaking to the issue. Unfortunately, there was no direct method to testthis possibility.

5.2 Opinion Predictions for Troops in Lebanon

The issue of troops in Lebanon was like defense spending topic in thatopinion both increased and decreased significantly. As usual, opinioncalculations began with the construction of infon persuasive forcecurves using AP dispatches scored for favoring more, same or less troops(Chapter 4). Then computations of poll percentages were made with onlyone modified persuasibility constant being the only variable parameter.Unfortunately, a good fit was not obtained. Examination of theprojections suggested that two modifications could improve thecalculations.

First of all, opinion favoring less troops seemed to be systematicallyunderestimated so scores favoring this position were given a refiningweight of 1.6 by least squares optimization. Persuasive force curvesincluding this weight for scores opposing more troops and a weight of1.0 for other infons are plotted in FIG. 5.13.

Furthermore, on Oct. 23, 1983 there was the unexpected explosion by aterrorist of a truck laden with explosives in the headquarters of theUnited States Marines in Beirut, Lebanon killing over 200 soldiers. Itseemed reasonable to suppose that the population reacted viscerally tothis report, feeling that some action was required, either putting moretroops in or pulling the ones there out. Therefore, a new persuasiveforce curve was computed including 80 paragraph equivalents forinformation favoring more troops (FIG. 5.14, lower frame) and noparagraphs favoring troop removal. For reference, the persuasive forcewithout this truck bombing infon is replotted from FIG. 5.13 (topframe).

One difference between the plots of FIGS. 5.13 and 5.14 is the beginningtime for the curves. FIG. 5.13 illustrates the fact that data retrievalsalways began at least 6 months before the date of the first poll dateunless that was before Jan. 1, 1977, the beginning date of the Nexisdata base for the AP. This was to assure that the residual effects ofprior messages was included in the opinion calculations.

For the persuasive force curves described above, the universalpersistence half-life of one day was used.

The opinion computations themselves then required the formulation of apopulation conversion model. Among these, the best for troops in Lebanonwas a "direct conversion model" (FIG. 5.15) where individuals could movedirectly from any one subpopulation to another without passing throughany intermediate subgroups, however transiently.

As was standard practice, the persuasive force curves in FIGS. 5.13 and5.14 were calculated using constants optimized by minimum MSD. Theseconstants included the modified persuasibility constant, the weight forscores favoring less troops (FIG. 5.16), and the persistence constant(FIG. 5.17).

The effect of news of the truck bombing on Oct. 23, 1983 was modeled byinjecting artificially, on that date, two infons of unknown magnitude, atruck bombing infon supporting more troops and a truck bombing infonfavoring less troops. The persuasive force curves for these two infonswere assumed to be characterized by the same one day persistence. Forcomparability, the content scores for these truck bombing infons weregiven in AP paragraph equivalents.

While the optimization for the truck bombing infon favoring more troopsshowed a marked improvement at a value of 80 AP paragraph equivalents(FIG. 5.16, lowest frame), there was no need even to invoke a truckbombing infon favoring troop withdrawal since the optimization showedthat the fit did not improve significantly as more paragraphs were addedup to about 40 AP paragraph equivalents (FIG. 5.17 lower frame). As thenumber of paragraphs increased above this number, the fit gotappreciably worse.

As for defense spending, the optimization for the persistence constantshowed two minima, one with a one day half-life and one with a 14 dayhalf- life (FIG. 5.17, upper frame). As argued, for defense spending,the lower minimum corresponding to a one day half-life was the chosenbecause it seemed unreasonable to have a yet shorter half-life.

The poll data from October 23 were omitted from the optimizationcalculations since that was on the date of the truck bombing infon.Given the rapid changes in the infon and poll data, accurate projectionson this date would have required that the infons and poll values beassigned to specific hours, an impossible task given the uncertaintiesin the timing of both the infons and poll itself.

Examination of the opinion projection patterns shows that one of thelargest effects was due to the introduction of the October 23 truckbombing infon. Without this infon, there would have been little opinionchange during the entire polling period, in disagreement with the polldata (FIG. 5.18). Once the infon was introduced, opinion fit quite well(FIG. 5.19).

Comparisons of projections with and without the truck bombing infonfavoring more troops (FIG. 5.20) showed that this infon did have a verylarge effect immediately after October 23. However, within a couple ofmonths, the effect was effectively dissipated with new opinionreflecting AP information at later times. This result is like that inFIG. 5.7 for defense spending where opinion moved more gradually toconform with the information structure.

As discussed above, there was no need to postulate any truck bombinginfons favoring fewer troops. That was because there was already a largeamount of information favoring fewer troops (FIG. 5.21, top frame). Theintroduction of 40 more paragraphs on October 23 did not significantlychange the shape of the persuasive force curve favoring less troops(FIG. 5.21, lower frame). Therefore, there was also little effect on theopinion calculations (FIG. 5.22). This result is quite like that forstories on waste and fraud for defense spending where the additionalnews was insignificant compared with other infons supporting lessspending.

In contrast, the truck bombing infon favoring more troops had a verylarge impact (FIG. 5.20) because there was very little other news infavor of that position (FIG. 5.14).

The improved fit by the addition of the truck bombing infon wasmanifested both by visual inspection of the opinion projections (FIG.5.19) and the by the RMSD (3.5% with the infon and 9.1% without, Table5.1). Table 5.1 (right hand column) also shows that the best projectionhad a probability less than 0.001 of being found by chance.

5.3 Opinion Predictions for the Democratic Primary

Besides being able to model policy issues, ideodynamics was alsoapplicable to electoral situations such as the Democratic primary of1984. The dispatches for this topic were scored both by the proximity ofbandwagon words to candidate names and by a count of the candidate names(Chapter 4). Therefore persuasive force curves were computed for bothanalyses. The bandwagon analysis yielded curves both favoring anddisadvantaging Mondale, Glenn or Others (FIGS. 5.23 and 5.24) while onlyinfons mentioning the three candidates were computed in the name countanalysis (FIG. 5.25). All scores were given the see weight of 1.0 andthe persistence constant had a one day half-life.

The first study used the bandwagon content analysis. Since thepersuasive force curves for this analysis both favored and disadvantagedall three groups of candidates, neither the sequential conversion modelmodel (FIGS. 1.2) nor the direction conversion model (FIG. 5.15) wasappropriate.

Instead, a mixture (FIG. 5.26) of both models was used. This model wasunique among the models in this book in including persons with NoOpinion. In other models, these persons were ignored, equivalent to theapproximation that most of them stayed uninvolved with the majority ofthe opinion changes being among those who already had an opinion.However, for the Democratic primary, it seemed unreasonable thatinformation unfavorable to a candidate would cause a supporter to favorany one of the other candidates. Rather, it seemed more plausible thatthe conversion would be to Undecided or No Opinion. Therefore, thiscategory was included for the bandwagon analysis. Information actuallyfavoring a candidate was presumed to be able to draw recruits from anyother subgroup.

This model had features of both the direct and sequential conversionmodels. A person favoring a particular candidate could be convinced tofavor another, either directly or by first becoming disenchanted andmoving temporarily into the undecided pool.

When content scores from the name count analysis were used, there was noinformation disfavoring a candidate and hence favoring No Opinion.Therefore, the same approximation was made as for the other studies inthis book. That is, the No Opinion subpopulation was assumed to consistof people who were unconcerned about the issue and were not responsiveto information about the campaign. Consequently, all persons with anopinion were normalized to 100%. As noted earlier, the inclusion orexclusion of the No Opinions was not crucial to the final curves sincethey only comprised around 10% of the total population at maximum.

With the exclusion of the No Opinions, the direct conversion model usedfor troops in Lebanon (FIG. 5.15) was the most reasonable (FIG. 5.27)since it could not be argued that support for any candidate should bepreceded by support for another.

For the opinion projections for the bandwagon analysis, the persuasiveforce curves (FIGS. 5.23 and 5.24) were constructed using thepersistence half-life of one day and the same weight for all scores. Theremaining unknown was the modified persuasibility constant. Both thepersistence and modified persuasibility constants were fixed by leastsquares optimization (FIG. 5.28, top two frames) using the populationconversion model of FIG. 5.26. The best modified persuasibility constantwas 1.5 per AP paragraph. The actual poll projections using theoptimized constants from FIG. 5.28 showed a reasonable fit (FIG. 5.29)with the most dramatic change being the decrease of almost two-fold insupport for Glenn.

For comparability, the same persuasibility constant was used to computethe poll projections using the name count analysis. These calculationswere based on the persuasive force curves of FIG. 5.25 and the model ofFIG. 5.27. The projections (FIG. 5.30) were obviously unsatisfactory.This is seen in the RMSD of 23.2%, over five times greater than the RMSDfor the bandwagon analysis (Table 5.1). In fact, when an optimizationwas performed to choose the best modified persuasibility constant forthe name count analysis, it was found that no improvements were possiblebeyond the situation of no opinion change corresponding to the modifiedpersuasibility constant having a value close to zero (FIG. 5.28, bottomframe).

The persuasive force curves of FIG. 5.25 give the reason for theinaccuracy. At essentially all times, there was a large excess of namementions for the minor candidates (bottom frame) as compared to the twofront runners. The actual numbers for the name counts is given in Table5.2. For example, Jesse Jackson was discussed in the news at a frequency(20%) between that of Glenn (17%) and Mondale (27%) due in large part tohis efforts to free a naval flier downed in Lebanon. Cranston's namemention of 13% was not far behind that of Glenn due, principally, to hisadvocacy of a nuclear freeze.

In fact, this excess in the mention of minor candidates was seenthroughout the polling period. Therefore, the model predicted a netmovement away from both major candidates toward the minor ones. This wasseen in the projected drop in support for both Glenn and Mondale withthe accompanying rise in the calculated popularity of the Others (FIG.5.30). Since the projected drop for Glenn was accompanied by the wrongmovements in the other two curves (FIG. 5.30), this fit was fortuitous.

From the optimizations for the bandwagon analysis, the proper choice forthe modified persuasibility constant gave an MSD over two-fold betterthan the estimate from a very small modified persuasibllity constantequivalent to no change in public opinion. This improvement was lessthan that for defense spending (five-fold) and troops in Lebanon(ten-fold). However, it was unreasonable to expect as much improvementbecause the poll values changed much more for those others examples soit was less appropriate for them to be approximated by no opinion changeduring the polling period. Since the polls changed much less for theDemocratic primary, an estimate of no opinion change gave a much betterfit. Nevertheless, this two-fold increase was substantial. The chancesof obtaining such a good fit by chance was less than 0.001 (Table 5.1).

The projection using the name count analysis was quite unsatisfactory.As mentioned above, the MSD of 540 was substantially worse than theprediction of no opinion change (MSD of 39). Indeed, the estimate was sobad that it could be obtained 34% of the time from random poll points(Table 5.1).

5.4 Opinion Predictions for the Economic Climate

The economic climate was the first of two economic issues studied. Asfor defense spending and the Democratic primary, there was no need tomake any adjustments by weighting the infon content scores from Chapter4. The persuasive force curves for this issue (FIG. 5.31) were computedusing the usual one day persistence half-life.

For this topic both the direct (FIGS. 5.15 and 5.27) and sequential(FIG. 1.2) conversion models were tried. Among these, the sequentialconversion model was better (FIG. 5.32).

With all infons weighted the same, the persistence constant (FIG. 5.33,lower frame) and the common modified persuasibility constant (FIG. 5.33upper frame) were both set by least squares optimization. This procedureled to an improvement in the MSD of approximately 10 times over theestimate from no opinion change as would be expected from a poll serieswhere the opinion values did vary significantly from the initial value.This is seen in the projections curves (FIG. 5.34). As with all othersatisfactory computations, the probability of such a fit by chance wasless than 0.001 (Table 5.1).

5.5 Opinion Predictions for Unemployment versus Inflation

The second economic topic was unemployment versus inflation. This issuewas like that for troops in Lebanon in that it was necessary to givedifferent infon scores different weights. By least squares optimizationit was found that scores favoring the importance of inflation shouldhave had a weight of 1.4 while those favoring equal importance shouldhave had a weight of 0.5. The remaining infon group supporting theimportance of unemployment had the reference weight of 1.0. Leastsquares optimization was also used to give an optimal persistenceconstant with a one day half-life. The persuasive force curvesreflecting these constants are presented in FIG. 5.35.

As for the other examples where the poll positions ranged from oneextreme through the middle to the other, it was possible to test boththe direct and sequential conversion models for population conversion.Of these the best was the direct conversion model (FIG. 5.36).

Least squares optimizations were used to determine the best values forall constants used in the construction of the persuasive force functionsand the modified persuasibility constant for the reference infonsfavoring importance of unemployment (FIGS. 5.37 and 5.38). Opinionprojections were then made using the optimized constants (FIG. 5.39).With the large opinion changes during the polling period, the estimateof no opinion change was so poor that the ideodynamic calculation couldgive an improvement in the MSD of four to five fold (FIG. 5.37, topframe). For this analysis also, there was a probability of less than0.001 that the fit could have been obtained by random poll points (Table5.1).

5.6 Opinion Predictions for Contra Aid

Contra aid was the only study in this book where opinion was fairlystatic during the time period of the study. The text analysis for Contraaid was unique, having been performed separately both by Fan and bythree graduate research assistants (Swim et al. in Chapter 4).Therefore, there were two sets of infon scores (FIGS. 5.40. and 5.41).Although Swim et al. gave scores to about twice as many paragraphs asFan, the two sets of scores revealed essentially the same overallinformation structure. For both sets of scores, least squareoptimizations showed that infons opposing aid needed to have a muchgreater weight than infons favoring aid. The optimized weights weresimilar, being 2.0 for the Fan scores and 2.4 for the Swim et al.scores. Also, optimization of the persistence constant gave the besthalf-lives with values greater than one day for both infon sets (seebelow). However, the one day half-life was satisfactory so that was thevalue used for calculating the persuasive force curves (FIGS. 5.40 and5.41).

Since there were only two positions, the only reasonable populationconversion model was that opinion favoring one side could convert peoplefavoring the other (FIG. 5.42). This was obviously the degenerate casewhere the direct and sequential conversion models collapsed into thesame model.

As with all other examples studied (FIG. 5.4, lower frame; FIG. 5.17,upper frame; FIG. 5.28, center frame; FIG. 5.33, lower frame; and FIG.5.38), the optimization for the persistence constant for Contra aid hadtwo minima, one at a one day half-life and the other ranging from sevento over 100 days. Since the one day minimum was common to all six issuesit was used as the consensus value for a universal persistence constantfor all issues.

The one day half-life was certainly reasonable since this was also thelowest minimum for the five examples besides Contra aid. It was alsomore reasonable to set the persistence constant for the other fiveissues for which the information structure and opinion both changedsignificantly. For Contra aid, there was little change in either opinion(Appendix B, Table B.6) or the ratio of favorable to unfavorableinformation (FIGS. 5.40 and 5.41). Therefore, much of any fit in thepersistence constant could have been to errors in the opinion polls.

Having justified the use of a one day universal persistence constantbased on six independent optimizations for six different topics, thefinal opinion projections used this half-life. These projections usedrefining weights of 2.0 and 2.4 for infons favoring less aid for the Fanand Swim et al. infons, respectively. These weights were optimized byminimizing the MSD (FIG. 5.43, center frame). The final curves (FIGS.5.44 and 5.45) showed relatively little change in opinion during theentire three year period once the modified persuasibility constant wasalso set by least square optimization (FIG. 5.43, top frame).Nevertheless, there was a substantial improvement in the fit overprojections of almost no change at all after the first poll point sincethe MSD was over three-fold lower than that at 0.001 per AP paragraphper day for the modified persuasibility constant. The probability havingof the quality of fit shown in FIGS. 5.44 and 5.45 by chance alone wereagain less than 0.001 (Table 5.1).

It was gratifying to find that two quite different text analyses couldboth give essentially indistinguishable opinion projections (FIGS. 5.44and 5.45). This result is not surprising since both analyses showed thatthere were the same approximate ratios of infons favoring and opposingContra aid throughout the polling period (FIGS. 5.40 and 5.41).

5.7 Summary of Constants Used in Poll Projections

This chapter has presented opinion projections for six quite disparateissues. The interpretations of these results are given in the next twochapters. For those discussions it will be useful to have a list of allthe parameters which were chosen by least squares optimization for eachof the cases (Table 5.3). All constants were optimized under the bestconditions for the other constants except for the issue of Contra aidwhere optimizations were performed using a one day persistencehalf-life. The constants which were optimized fell into the followingcategories:

(1) Persistence constant. This constant measured the ability of an APinfon to continue to exert its effect after the date of the dispatch. Asnoted in Section 5.6 above, the optimal value for this constant was aone day half-life for five out of six issues and this same value wasalso satisfactory for the sixth. Thus the optimizations for the sixissues yielded a universal one day half-life for the persistenceconstant.

(2) Modified persuasibility constants. For modified persuasibilityconstants, the least squares optimal value was calculated for areference set of infon scores. Any needed variations in these constantsfor infons favoring different positions were incorporated into therefining weights. When all weighting values were the same, as was thecase for defense spending, the Democratic primary, and the economicclimate, any one of the positions could have been chosen as thereference position.

(3) Refining weights. The refining weight for a position was theconstant by which all infon scores for a position were multiplied beforeconstruction of the persuasive force functions. This weight includedboth differences between the persuasibility of the public for differentpositions and imperfections in the infon scoring (Chapter 1 and AppendixA).

Therefore, when the public was as easy to persuade for all positions andwhen all infons were scored correctly, all refining weights had thevalue of 1.0. In some cases the weights were different for differentinfons scores. An example was the issue of troops in Lebanon where theweight was 1.6 for scores favoring less troops and 1.0 for all otherscores. This meant that a paragraph favoring less troops was 1.6 timesas effective as a paragraph favoring same or more troops.

5.8 Summary of Statistics for Poll Projections

Statistically speaking, the estimate for a value is the mean withdeviations from that mean being characterized by the standard deviation.The standard deviation for a set of values is computed by taking thesquare root of the squares of the deviations of the individual pointsfrom the mean. Similarly, if the estimate for an opinion percentage isgiven by the ideodynamic prediction, then statistical deviations can berepresented by the RMSD computed by taking the square roots of thesquares of the deviations between actual poll measurements and theideodynamics predictions. Therefore, assuming that the differencesbetween two values are statistical, the RMSD is like the standarddeviation so that differences between the predicted and measured pollvalues should be within one RMSD about 68% of the time and within twoRMSD about 95% of the time.

The RMSD in Table 5.1 are the minimum values corresponding to the bestparameters chosen. These values were 3.5% for troops in Lebanon, 4.3%for the Democratic primary, 4.7% for Contra aid, 6.6% for the economicclimate, 7.2% for defense spending (1977-1984), and 7.7% forunemployment versus inflation. The errors decreased with the time spanof the projections. For instance, the most accurate computations werefor troops in Lebanon and the Democratic primary where the poll seriesonly covered 4 and 7 months respectively. The analyses for the otherfour examples spanned periods from three to seven years. It was quitepossible that text and its interpretation changed with time so that thetext analyses should have been modified as time proceeded. Also, themodified persuasibility constants might have been dependent on time,changing slowly over the period of years so they too might have variedwith time.

For comparison, national polls frequently have reported errors due tofinite sample size in the range of 4% at the 95% confidence level. Thisis equivalent to two standard deviations from the reported values.Therefore, their equivalent to the RMSD would have been about 2% insteadof the 3.5% to 7.7% in Table 5.1. However, besides sample size errors,there are also systematic errors in the polls due to question wording,question sequence, etc. Therefore, the 2% standard error is a minimumerror in the opinion measurements.

The importance of these systematic errors is seen in the poll data ofFIG. 5.10 where polls were taken at close intervals by different,respectable polling organizations. There are fluctuations in the rangeof 5 to 10% which may have been real but which may also have been due todifferences in the polling instruments. If actual measurements ofopinion can have errors in the range of 5 to 10%, then the ideodynamicerrors are in the same range and may be as good as those from opinionpolls.

From the summary data in Table 5.1, it is also clear that the bestideodynamic opinion projections had very little probability (less than0.001) of being obtained by chance alone. In addition, all projectionswere also better than the model of no opinion change after the firstpoll point. This was seen in the improvements in the MSD as the modifiedpersuasibility constant increased above values near zero (top frames inFIGS. 5.4, 5.16, 5.28, 5.33, 5.37, and FIG. 5.43).

Table 5.1

Statistical comparisons for opinion projections. All computations usedthe constants in Table 5.2 except that the memory half-life was one dayfor all calculations including the two for Contra aid.

Table 5.2

Candidate name counts in dispatches retrieved for the Democraticprimary. The name counts were for all the retrieved AP dispatches. Theothers category was the sum of the results for the others.

Table 5.3

Optimal constants for opinion projections. This table summarizes theoptimal values determined by minimization of the MSD (FIGS. 5.7, 5.19,5.20, 5.31, 5.36, 5.40, 5.41 and 5.46) and used for the opinionprojections. The only exception was for Contra aid where the half-lifeof one day was used for the projections. The modified persuasibilityconstant k'₂ had units per AP paragraph per day and the memory half-livewas measured in days. Unless otherwise noted, all infons had the weightof 1.0.

                                      TABLE 5. 1                                  __________________________________________________________________________                          1000 Monte Carlo Estimates                                                    (Assuming Random Poll Values)                                                        Number of                                                       Ideodynamic   Standard                                                                             Probability                                              Estimate                                                                             Mean   Deviations                                                                           of                                                       Ideodynamic                                                                          Monte Carlo                                                                          from   Obtaining                                                RMSD   RMSD   Ideodynamic                                                                          Ideodynamic                                              (in Poll                                                                             (in Poll                                                                             MSD to MSD                                                      Percentage                                                                           Percentage                                                                           Monte Carlo                                                                          by                                        Issue          Points)                                                                              Points)                                                                              Mean MSD                                                                             Chance                                    __________________________________________________________________________    Defense Spending (1977-1984)                                                  Scores More, Same, Less                                                       692 retrievals 7.2    24.3   5.2    <0.001                                    325 retrievals 9.4    24.3   4.9    <0.001                                    383 retrievals 10.3   24.3   4.7    <0.001                                    Scores More, Less                                                             692 retrievals 8.3    24.3   5.1    <0.001                                    Troops in Lebanon                                                             With cTruckBombMore                                                                          3.5    26.6   4.2    <0.001                                    No Truck Bomb Infons                                                                         9.1    26.6   3.7    <0.001                                    Democratic Primary                                                            Bandwagon Text Analysis                                                                      4.3    19.9   3.7    <0.001                                    Name Count Scoring                                                                           23.2   22.0   0.3    0.34                                      Economic Climate                                                                             6.6    23.3   5.4    <0.001                                    Unemployment vs. Inflation                                                                   7.7    27.1   4.8    <0.001                                    Contra Aid                                                                    Fan Text Analysis                                                                            4.7    32.9   3.3    <0.001                                    French et al Text Analysis                                                                   5.1    32.9   3.3    <0.001                                    __________________________________________________________________________

                  TABLE 5. 2                                                      ______________________________________                                                  Name Count                                                          Candidate   Actual number                                                                             Percent of Total                                      ______________________________________                                        Mondale     959         27                                                    Glenn       632         17                                                    Others:     2018        56                                                    Askew       189          5                                                    Cranston    450         13                                                    Hart        251          7                                                    Hollings    255          7                                                    Jackson     740         20                                                    McGovern    133          4                                                    ______________________________________                                    

                                      TABLE 5. 3                                  __________________________________________________________________________                               Infons                                                            Memory                                                                             Persuasbility                                                                        with Weights ≠ 1.0                                          Constant                                                                           Constant                                                                             Position Favored                                                  m    k'.sub.2                                                                             Weight                                                                            by AP Infons                                   __________________________________________________________________________    Defense Spending (1977-1984)                                                                 1.0  0.6                                                       Scored More, Same, Less                                                       692 retrievals                                                                Troops in Lebanon                                                                            1.0  4.5    1.6 Less Troops                                    with cTruckBombMore        .sup.a (80                                                                        Truck Bomb Imply                                                              Less Troops)                                   Democratic Primary                                                                           1.0  1.5                                                       Bandwagon Text Analysis                                                       Economic Climate                                                                             1.0  0.09                                                      Unemployment vs. Inflation                                                                   1.0  7      0.5 Unemployment and                                                              Inflation Equally                                                             Important                                                                 1.4 Inflation Important                            Contra Aid                                                                    Fan Text Analysis                                                                            41   1.2    2.0 Oppose Aid                                     French et al Text Analysis                                                                   7    1.4    2.4 Oppose Aid                                     __________________________________________________________________________     .sup.a This line means that the infon favoring more troops due solely to      news of the October 23, 1983 truck bombing had a value equivalent to 80 A     paragraphs favoring more troops.                                         

FIG. 5.1. Persuasive forces of AP infons scored for favoring more, sameand less defense spending. Of the 692 retrieved dispatches, 272 hadscores favoring more, same and less spending (text analysis from SectionC.2-3, Appendix C). These infon scores were converted into persuasiveforce curves assuming that all infons had the same weight of 1.0 andthat the persistence half-life was one day. As in FIG. 1.1, the curvesfor separate infons were added together to give the net forces in thethree directions. The heights of the curves for individual infons,before the addition, were the sums of the paragraph scores (see Chapter3).

FIG. 5.2 Persuasive forces of AP infons scored for favoring more andless defense spending. The same 692 dispatches scored for FIG. 5.1 wererescored as favoring only more and less spending (see Section C.2-3,Appendix C) instead of more, same and less spending. Of these 692dispatches, 280 had scores favoring more and less spending. These scoreswere converted into persuasive force curves as in FIG. 5.1 using thepersistence half-life of one day and the weight of 1.0 for all infons.

FIG. 5.3 Opinion on defense spending from dispatches scored to favormore, same and less spending. The projections for the threesubpopulations favoring more, same and less defense spending (solidlines) began with the opinion measurements of the first poll in March1977 and continued using the population conversion model of FIG. 1.2 andthe persuasive forces shown in FIG. 5.1. All 272 scores favoring more,same and less spending were used for the computations. The modifiedpersuasibility constant was 0.6 per AP paragraph per day. Calculationswere at 24 hour intervals. For comparison, the squares show the resultsof 22 published polls (Table B1, Appendix B).

FIG. 5.4 Constant optimization curves for defense spending. Infons from272 AP messages scored as favoring more, same and less spending wereused to project opinion assuming a number of modified persuasibilityconstants k'2 (top frame) and persistence half-lives (bottom frame). Forall computations, the MSD (see Section A11, Appendix A), were obtainedwith infons favoring all three positions having the weight of 1.0. Theoptimal modified persuasibility constant was 0.6 per AP paragraph perday and the best persistence constant was one day. Both curves wereobtained using the optimal value for the other constant.

FIG. 5.5 Opinion from a subset of AP dispatches scored to favor more,same and less defense spending. The projections (solid lines) were thesame as those in FIG. 5.3 except that only 325 of the original 692 APdispatches were used for calculating the persuasive forces. The sameconstants were used as in FIG. 5.3. Of the 325 dispatches, 131 had atleast one paragraph with a score favoring more, same or less spending.For comparison, the same poll data are plotted as in FIG. 5.3.

FIG. 5.6 Opinion from another subset of AP dispatches scored to favormore, same and less defense spending. The projections (solid lines) werethe same as those in FIG. 5.5 but used 383 essentially non-overlappingdispatches from among the original 692 used for FIG. 5.3. Of the 383dispatches, 147 had at least one paragraph with a score favoring more,same or less spending. Actual poll values are shown as squares.

FIG. 5.7 Opinion on defense spending assuming the entire populationfavored more spending at the time of the first scored AP infon inJanuary 1977. The persuasive forces were the 272 scores used for FIG.5.3 favoring more, same and less spending. All conditions for thecalculation were unchanged from those for that figure except that thepopulation was not assumed to begin with the first actual poll point butrather with the artificial initial condition of 100% of the populationfavoring more spending at the time of the first scored infon. Theprojections based on 100% favoring more spending (solid lines) areplotted together with those based on the first poll point (dotted lines,replotted from FIG. 5.3).

FIG. 5.8 Opinion on defense spending from dispatches scored to favormore and less defense spending only. The projections (solid lines) forthe three groups favoring more, same and less spending began with theopinion measurements of the first poll in March 1977 and continued usingthe 280 scores for the persuasive forces shown in FIG. 5.2. As for FIG.5.3, the model was given in FIG. 1.2 and the modified persuasibilityconstant was 0.6 per AP paragraph per day, the persistence half-life wasone day, and all infons had the weight of 1.0. Actual poll points areshown as squares.

FIG. 5.9. Persuasive forces of AP infons from 1977-1986 scored forfavoring more, same and less defense spending. The text analysis usedfor FIG. 5.1 was applied without change to 1067 AP dispatches retrievedfrom Jan. 1, 1981 to Apr. 12, 1986. The resulting 779 new scores weremerged with the 272 from the retrieval used for FIG. 5.1. The combinedscores were used to calculate persuasive force curves using the same oneday half- life and the same weight of 1.0 for all infons. The paragraphscores were normalized to account for the fact that less than 100% ofthe identified stores were collected in both retrievals.

FIG. 5.10 Opinion on defense spending from 1977 to 1986. The persuasiveforce curves of FIG. 5.9 were used to calculate public opinion using theconditions described for FIG. 5.3 (solid line). For comparison, 62published poll points were plotted including the 22 used for FIG. 5.3(squares).

FIG. 5.11 Constant optimization curve for the contributions fromparagraphs favoring less defense spending. Scores favoring less defensespending were given different weights from those favoring more or samespending which both had the same weight of 1.0. The MSD was plottedagainst various relative weights for the less spending scores.

FIG. 5.12 Effect of stories on waste and fraud on public opinion ondefense spending. A persuasive force curve (top frame) was constructedwhere all paragraphs on defense waste and fraud (see Section C.2-5,Appendix C) had the same value as paragraphs directly advocating aposition on defense spending. The curve on defense waste and fraud (topframe) was added to the persuasive force curve for infons directlysupporting less spending (FIG. 5.9, bottom frame) to give the combinedpersuasive force favoring less spending (center frame). Opinion favoringmore defense spending was computed using the combined persuasive force(center frame), in place of the persuasive force lacking the paragraphson waste and corruption (FIG. 5.9, bottom frame). The projections bothwith (solid line) and without (dotted line) the paragraphs on waste andfraud are plotted in the bottom frame. The curve without waste and fraudis the same as FIG. 5.10 (top frame).

FIG. 5.13 Persuasive forces for troops in Lebanon from AP infons only.AP dispatches were scored to favor more, same and less troops (SectionC.3, Appendix 3). The dispatch scores were converted into infon forcesusing a persistence half-life of one day. The paragraph scores forinfons favoring less troops were each given a weight of 1.6 relative tothe infons favoring more and same troops which both had the same weightof 1.0. The extra tic on the horizontal axis between October andNovember was at October 23. The plot begins with the date of the firstretrieved dispatch.

FIG. 5.14 Persuasive forces for troops in Lebanon from AP infons withand without a truck bombing infon favoring more troops. The APdispatches scores without an extra truck bombing infon (top frame) werethe same as in FIG. 5.13 except that the plot began with the date of thefirst available poll point (Table B.2, Appendix B). In the bottom frame,there was the addition of a truck bombing infon favoring more troopsequivalent to 80 AP paragraphs on Oct. 23, 1983, the date of the extratic on the horizontal axis. The truck bombing infon also had a one daypersistence half-life.

FIG. 5.15 Population conversion model for actions of infons favoringmore, same and less troops in Lebanon. The boxes denote subpopulations"believing" in more, same, or less troops. The arrows indicate theopinion conversions due to the persuasive forces (beginning with G andshown in FIGS. 5.13, 5.14, 5.21) favoring the same positions.

FIG. 5.16 Optimizations for the modified persuasibility constant, theweight for paragraphs favoring less troops and the value of the truckbombing infon favoring more troops. The best modified persuasibllityconstant k'₂ giving the lowest MSD was 4.5 per AP paragraph per day (topframe); the optimal weight for infons favoring less troops was 1.6 whenall other infons had weights of 1.0 (center frame), and the mostfavorable value for the Oct. 23, 1983 infon favoring more troops wasequivalent to 80 AP paragraphs (bottom frame). For all calculations, thepersistence half-life was one day, and there was assumed to be no truckbombing infons favoring less troops. In addition, all optimizations wereperformed at the best values for the other constants.

FIG. 5.17 Optimization curves for the persistence half-life and thevalue of the truck bombing infon favoring less troops. The lowest MSDgiving the best half-life was 1 day (top frame). There was essentiallyno change in the MSD from almost zero to almost 40 AP paragraphequivalents for the Oct. 23, 1983 truck bombing infon favoring lesstroops (bottom frame). Both optimizations were performed at the bestvalues for the other constants described in FIG. 5.16 and FIG. 5.17.

FIG. 5.18 Opinion On troops in Lebanon assuming only AP infons. Theprojections beginning with the first poll point (solid lines) used thethree persuasive force curves of FIG. 5.13, the model of FIG. 5.15, andthe optimal constants of FIGS. 5.16 and 5.17 with the exception thatboth truck bombing infons favoring more and less troops were bothomitted. Calculations were at 6 hour intervals. Actual poll points areplotted as squares. October 23 is indicated by the tic between Oct. andNov.

FIG. 5.19 option on troops in Lebanon with a truck bombing infonfavoring more troops The computations (solid lines) were the same as forFIG. 5.18 except that a truck bombing infon on October 23 favoring moretroops at the optimal value of 80 AP paragraph equivalents (see FIG.5.16, bottom frame) was also included. Therefore, the persuasive forcecurve favoring more troops was that in the lower frame of FIG. 5.14. Theother persuasive force curves are in the bottom two frames of FIG. 5.13.

FIG. 5.20 Comparison of Opinion projections with (solid line) or withoutthe truck bombing infon (dotted line) favoring more troops. The curvesin FIGS. 5.18 and 5.19 were plotted together.

FIG. 5.21 Persuasive forces from AP infons with and without a truckbombing infon favoring less troops. The AP dispatches scores without anextra truck bombing infon (top frame) were the same as the bottom framein FIG. 5.13 except that the plot began with the date of the firstavailable poll point on troops in Lebanon (Table B.2, Appendix B). Inthe bottom frame of this figure, there was the addition of a truckbombing infon favoring less troops equivalent to 40 AP paragraphs onOct. 23, 1983, the date of the extra tic on the horizontal axis. Thetruck bombing infon also had a one day persistence half-life.

FIG. 5.22 Opinion projections with and without a truck bombing infonfavoring less troops. Projections were made for public opinion as inFIG. 5.19 except that there was the addition of a truck bombing infon onOctober 23 favoring less troops equivalent to 40 AP paragraphs (dottedlines). For this projection, the persuasive force curve favoring lesstroops was that in the bottom frame of FIG. 5.21. For comparison,projections without this truck bombing infon favoring less troops arereplotted from FIG. 5.19 (solid lines).

FIG. 5.23 Persuasive forces favorable to Democratic presidentialcandidates from AP paragraphs scored using bandwagon words. Paragraphswere scored as favoring Mondale, Glenn and Others if candidate nameswere close to favorable combinations of bandwagon words (see SectionC.4-1, Appendix C). The infon force curves were calculated using a oneday persistence half-life and the weight of 1.0 for all infons.

FIG. 5.24 Persuasive forces unfavorable to Democratic presidentialCandidates from AP paragraphs scored using bandwagon words. Paragraphswere scored as unfavorable to Mondale, Glenn and Others if candidatenames were close to combinations of unfavorable bandwagon words (seeSection C.4-1, Appendix C). The infon force curves were calculated usinga one day persistence half-life and all infons with the weight of 1.0 asin FIG. 5.23.

FIG. 5.25 Persuasive forces of AP infons scored by name count only.Paragraphs mentioning Mondale, Glenn and Others were scored as discussedin Section C.4-2, Appendix C). The infon force curves were calculatedwith a one day persistence half-life and all infons with the weight of1.0.

FIG. 5.26 population conversion model for actions of infons scored usingbandwagon words. The boxes denote the subpopulations underconsideration. The words in the boxes began with B to refer to those"believing" or having an opinion favoring Mondale, Glenn, Others or Noopinion. The persuasive forces at any particular time were read from thecurves in FIGS. 5.23 and 5.24 and were favorable or unfavorable toMondale, Glenn or Others. Words describing these forces began with Gwith favorable information denoted by Pro and unfavorable by Con. Thecandidate or group of candidates was indicated by the first threeletters of the name or group. The arrows indicated the opinionconversions due to the persuasive forces shown in FIGS. 5.23 and 5.24.

FIG. 5.27 Population conversion model for actions of infons scored byname count only. The boxes denote the subpopulations underconsideration. The words in the boxes began with B to refer to those"believing" or having an opinion favoring Mondale, Glenn or Others. Thearrows indicate the opinion conversions due to the persuasive forces(beginning with G and shown in FIG. 5.25).

FIG. 5.28 Optimization curves for constants for the Democratic primary.Bandwagon analysis: Using the population conversion model of FIG. 5.26and the persuasive forces of FIGS. 5.23 and 5.24, the best modifiedpersuasibility constant k'₂ giving the lowest MSD was 1.5 per APpargraph per day (top frame) for the bandwagon analysis; and the optimalpersistence half- life for the same analysis was one day (center frame).The two optimizations were each performed under the best conditions forthe other constant. Name count analysis: The bottom frame shows that thebest value for the modified persuasibility constant was very close tozero using a persistence half-life of one day using the persuasiveforces of FIG. 5.25 and the population conversion model of FIG. 5.27.

FIG. 5.29 Opinion On Democratic candidates when infons were scored bythe bandwagon analysis. The projections (solid lines) for the threeGroups favoring Mondale, Glenn and others began with the opinionmeasurements of the first poll on Jun. 19, 1983 and continued using thepersuasive forces shown in FIGS. 5.23 and 5.24, the model of FIG. 5.26,and the optimized modified persuasibility constant of 1.5 per APparagraph per day. Calculations were at 6 hour intervals. Actual pollpoints are plotted as squares.

FIG. 5.30 Opinion On on Democratic candidates when infons were scored byname count only. The projections for the three groups favoring Mondale,Glenn and others (solid line) began with the opinion measurements of thefirst poll on Jun. 19, 1983 and continued using the persuasive forcesshown in FIG. 5.25, the model of FIG. 5.27 and the same modifiedpersuasibility constant of 1.5 per AP paragraph per day used for FIG.5.29. Calculations were at 6 hour intervals. Actual poll points areplotted as squares.

FIG. 5.31 Persuasive forces from AP paragraphs favoring better, same andworse economic conditions. Paragraphs favoring these three positionswere scored (see Section C.5, Appendix C) and used for the constructionof persuasive force curves employing a one day persistence half-live andequal weights of 1.0 for all infons.

FIG. 5.32 Population conversion model for actions of infons favoringbetter, same and worse economic conditions. The boxes denote thesubpopulations under consideration. The words in the boxes began with Bto refer to those "believing" or having an opinion favoring better, sameand worse conditions. The arrows indicate the opinion conversions due tothe persuasive forces (beginning with G) shown in FIG. 5.31.

FIG. 5.33 Optimization curves for constants for the economic climate.Using the population conversion model of FIG. 5.32 and the persuasiveforces of FIG. 5.31, the best modified persuasibility constant k'₂giving the least MSD had a value of 0.09 per AP paragraph per day (topframe). The optimal persistence half-life was one day (bottom frame).Both optimizations were performed under the most favorable conditionsfor the other constant.

FIG. 5.34 Opinion on economic climate. The projections for the threegroups feeling that the climate was better, same or worse (solid lines)began with the opinion measurements of the first poll on Mar. 6, 1981and continued using the persuasive forces of FIG. 5.31, the populationconversion model of FIG. 5.32, and the optimal constants from FIG. 5.33.The computations were performed every 24 hours. Actual poll points areplotted as squares.

FIG. 5.35 Persuasive forces of AP infons favoring unemployment moreimportant, equal importance and inflation more important. Paragraphsfavoring these three positions were scored (see Section C6, Appendix C)and used for the construction of persuasive force curves employing a oneday persistence half-life and the following weights for infonssupporting different positions: 1.0 for infons favoring unemploymentimportant, 0.5 for infons favoring equal importance, and 1.4 for infonsfavoring inflation important.

FIG. 5.36 Population conversion model for actions of infons favoringunemployment important, equal importance and inflation more important.The boxes denote the subpopulations under consideration. The words inthe boxes began with B to refer to those "believing" or having anopinion favoring the three positions. The arrows indicate the opinionconversions due to the persuasive forces (beginning with G) shown inFIG. 5.35.

FIG. 5.37 Optimization curves for the modified persuasibility constantand the infon weighting constants for unemployment versus inflation.Using the population conversion model of FIG. 5.36 and the persuasiveforces of FIG. 5.35, the best modified persuasibility constant givingthe least MSD had a value of 7 per AP paragraph per day (top frame). Theoptimal weight for infons favoring equal importance was 0.5 (centerframe) and that for infons favoring inflation important was 1.4 (bottomframe). All optimizations were performed under the most favorableconditions for the other constants and with a one day persistencehalf-life.

FIG. 5.38 Optimization curve for the persistence constant forunemployment versus inflation. Using the same conditions and the optimalconstants from FIG. 5.37, a persistence constant of one day gave theleast MSD.

FIG. 5.39 Opinion favoring unemployment more important, equal importanceor inflation more important. The projections for the three opinions(solid lines) began with the measurements of the first poll on Mar. 22,1977 and continued using the persuasive forces shown in FIG. 5.35, thepopulation conversion model of FIG. 5.36 and the optimal constants fromFIGS. 5.37 and 5.38. The computations were performed every 24 hours.Actual poll points are plotted as squares.

FIG. 5.40 Persuasive forces of AP infons scored by the author asfavoring and opposing Contra Aid. Paragraphs for these two positionswere scored by Fan (see Section C.7-1, Appendix C) and used for theconstruction of persuasive force curves employing a one day persistencehalf-life, a weight of 1.0 for infons favoring aid and a weight of 2.0for infons opposing aid.

FIG. 5.41 Persuasive forces of AP infons scored by the Swim, Miene andFrench as favoring and opposing Contra Aid. Paragraphs for these twopositions were scored (see Section C.7-2, Appendix C) and used for theconstruction of persuasive force curves employing a one day persistencehalf- life, a weight of 1.0 for infons favoring aid and a weight of 2.4for infons opposing aid.

FIG. 5.42 Population conversion model for actions of infons favoring andopposing contra aid. The boxes denote the subpopulations underconsideration. The words in the boxes began with B to refer to those"believing" or having an opinion favoring or opposing Contra aid. Thearrows indicate the opinion conversions due to the persuasive forces(beginning with G) shown in FIGS. 5.40 and 5.41.

FIG. 5.43 Constant optimization curves for Contra aid. The MSD wascalculated for opinion projections using both infon scores obtained bythe author (Fan) as plotted in FIG. 5.40 (solid lines) and by Swim,Miene and French as plotted in FIG. 5.41 (dotted lines). For allcalculations, the population conversion model was the one in FIG. 5.42.A one day persistence half-life was used for determinations of the bestmodified persuasibility constants k'₂ favoring Contra aid (top frame)(1.2 per AP paragraph per day for Fan infons and 1.6 for Swim et al.infons), and calculations for the optimal weights for infons opposingContra aid (center frame) (2.0 for Fan infons and 2.4 for Swim et al.infons). The optimal values for the other constants were used forcomputing the best persistence half-lives (bottom frame) (41 days forFan infons and 7 days for Swim et al. infons).

FIG. 5.44 Opinion favoring and opposing Contra aid using infon scores bythe author. The projections for the two opinions (solid lines) beganwith the opinion measurements of the first poll on Aug. 20, 1983 andcontinued using the persuasive forces shown in FIG. 5.40, the populationconversion model of FIG. 5.42, a persistence half-life of one day, andoptimal values for the modified persuasibility constant favoring Contraaid and the weight for infons opposing aid (FIG. 5.43, solid lines).Computations were performed every 24 hours. Actual poll points areplotted as squares.

FIG. 5.45 Opinion favoring and opposing Contra aid using infon scores bythe Swim, Miene and French. The projections for the two opinions (solidlines) began with the opinion measurements of the first poll on Aug. 20,1983 and continued using the persuasive forces shown in FIG. 5.41, thepopulation conversion model of FIG. 5.42, a persistence half-life of oneday, and optimal values for the modified persuasibility constantfavoring Contra aid and the weight for infons opposing aid (FIG. 5.43dotted lines). Computations were performed every 24 hours. Actual pollpoints are plotted as squares.

CHAPTER 6 Methodological Significance of Work

The previous three chapters have applied computer text analyses andideodynamics equations to project expected public opinion. The presentchapter examines the methodological implications of the procedures used.The next chapter will consider the theoretical significance of theresults.

The studies in this book focused on public opinion where there wasrelatively little social or economic cost for a person changing hismind. A deliberate choice was made to avoid economic issues, includingproduct purchase, because such activities required the weighing of suchcomplicating factors as competing uses for financial resources. Messagesdue to these important factors were difficult to study directly.

The introduction of innovations into society was also not studiedbecause they frequently involved not only economic considerations butalso other social factors. While economics was obviously important forthe classical studies of the adoption of hybrid corn (Ryan and Gross,1943), investigations on the acceptance of family planning (Berelson andFreedman, 1964) also had to contend with complex societal forces relatedto sexuality and reproduction.

In contrast, people were unlikely to have deep convictions for theissues studied in this book (Chapter 3). Members of the population as awhole clearly have no good idea how much should be spent for defense.Troops were only briefly in Lebanon with little time to form ingrainedprejudices. None of the Democrats running for president had ever heldthat office so none had the advantages of incumbency. The public waswell aware that the economy could change so there was no reason to feelthat the economic climate should always be good or bad or thatunemployment should always be more or less important than inflation. Aswith Lebanon, the Contras were in a distant land with which fewAmericans had little personal contact and inherent opinion.

These theoretical arguments for opinion malleability and volatility werebolstered by actual findings that there were substantial opinionmovements in all examples except that of Contra aid. In fact, one of thereasons for studying these other instances was that opinion did change,thereby providing the most critical tests of the methodology. However,it was also useful to demonstrate that the calculations could projectunchanging opinion for Contra aid when constancy was actually observed.The model would definitely have been weakened by data showing that therewas a change in the ratio of favorable to unfavorable information whilethere was no simultaneous change in opinion.

Although these studies showed the applicability of the calculations toissues where people hold shallow and changeable convictions, the samemethodology should theoretically succeed even for more firmly heldbeliefs if all the relevant messages available to the public can becoded. For firmly held beliefs, the persuasibility constants wouldsimply be decreased so that more information would be needed to cause anopinion shift.

6.1 Validation of Ideodynamics

Since no applications of ideodynamics have been described previously, itwas important to validate the model using not only the logic of theargument but also empirical tests.

Such tests benefitted from one of the unusual capabilities of the model,its ability to consider the time dimension divided into infinitesimallysmall intervals. As noted in Chapter 2, this was possible becauseopinion calculations were not based solely on the information availableto the population but also on the opinion in the previous time interval.The use of small time intervals, like the 6 or 24 hours in this book,permits the tracking of rapid changes like that for troops in Lebanon(FIG. 5.19) where opinion favoring more troops changed from under 7% toover 30% and back down to under 9% all within a few days.

The use of small time intervals is important to empirical tests ofideodynamics because the number of opinion predictions increases and thetiming of the computed opinion values becomes more precise as theintervals for the opinion calculations decrease in size. The result ismore opinion Projections at more closely defined times. These veryprecise predictions can then be tested against measured poll data.

In the actual cases studied, the number of times at which opinion valueswere calculated for each time series ranged from approximately 400 fortroops in Lebanon to over 3000 for defense spending. Therefore, themodel could be tested by its ability to mimic poll data for hundreds tothousands of time points.

For each of the cases in this book, it was necessary to assign one tothree independent parameters in addition to the persistence constantwhich was set to have a one day half-life for all studies. Therefore,the minimum number of poll points needed to set the parameters should bethose sufficient to give one to three independent opinion measurements.

At each poll time, there were two to four polled positions. Opinion forone of the positions could be determined by subtraction from 100% so thenumber of independent opinion measurements was one less than the polledpositions. As a result, a poll series with three points would yield aminimum of three independent poll percentage values (the Contra casewith only the two positions of pro- and con-aid). Therefore, three pollpoints should be the most needed to establish three parameters.

In addition, the poll percentages at the earliest time in the series wasused as the starting point for the calculations so opinion values atthis time were the boundary conditions also needed for setting theparameters. Therefore, in the worst case, a poll series with four timepoints should uniquely define the maximum of three parameters. One ofthese poll series would correspond to the initial poll point, and theother three poll points would be the maximum needed to set threeparameters. In the best cases, with only one parameter aside from theconsensus half-life of one day (defense spending, Democratic primary,and economic climate), only two poll points were needed, in principle,to define the parameters.

Once the parameters were set, any additional poll points could no longerbe fit by adjusting the parameters and would critically test the modelempirically. This estimate of needing poll series of two to four pointsto establish the parameters is only approximate because the opinionvalues are not truly independent. Instead, opinion at any time isdependent on opinion at earlier times. Nevertheless, these arguments doindicate that series with 8 (Democratic primary) to 62 (defensespending) time points did indeed provide meaningful empirical tests ofthe model.

Since ideodynamics uses a number of approximations, it will not alwaysbe clear which are faulty if the model does not give satisfactorypredictions. In contrast, generally accurate calculations for a numberof issues will render plausible the total constellation ofapproximations.

The quality of the fit shown in the tables, figures and section 5.9 ofChapter 5 indicate that the methodology has good predictive powers. Itwas also significant that the methods in this book were successful forsix out six cases tested. In consequence, the collection ofapproximations used for the computations stand a reasonable chance ofbeing valid.

Since the empirical tests for ideodynamics involved comparisons betweenpoll results and opinion computed from information available to thepopulation, it was essential that scores for persuasive messages beobtained independently of opinion measurements. For this reason, threeprecautions were taken:

(1) The conditions for the infon scoring of Chapter 4 were developed byexamining AP stories in random order so that the analyst would not betempted--consciously or unconsciously--to bias scores with the goal offitting poll results.

(2) The same computer scoring was applied to all stories so any changesin scoring rules could not be applied preferentially to a chosen subsetof stories in order to achieve scores which would result in good opinionpredictions.

(3) For the example of Contra aid, the Roper Center was first contactedto establish that there was likely to be enough polls to construct areasonable time series for the model testing. However, no poll valueswere actually obtained until the text analysis was finished both by Fanand by Swim, Miene and French. Only after completion of the textanalysis were actual poll percentages obtained from the Roper center.Therefore, for this example, there was absolutely no way for theanalysts to adjust the scoring to match measured poll values.

6.2 Data and Issues for Successful Ideodynamic Calculations

Since methodological development is one of the significant aspect ofthis book, it is useful to consider the appropriate conditions forapplying the methodologies:

One important suggestion from the studies presented above is thatideodynamics is applicable to a wide variety of issues. After all, themodel was successful for issues drawn from areas as diverse as foreignpolicy (troops in Lebanon and Contra aid), economic issues (economicconditions and the importance of unemployment versus inflation),domestic policy (defense spending), and political campaigns (Democraticprimary).

The accuracy ideodynamics derives from the fact the calculations includeall relevant persuasive messages. All the issues just mentioned sharedthe condition that the mass media were likely to contain the majority ofthe relevant persuasive messages. Other informational sources such asbooks or education in schools could not keep abreast of the pertinentnews as it was being generated.

The main other sources which had the potential to inform as rapidly werepersonal experience and underground means of communication such asrumors.

Personal experience was unimportant for all the issues studied. For theDemocratic primary in the early stages, defense spending, Contra aid,and troops in Lebanon, it was plausible that the mass media were likelyto be the primary sources of all pertinent communications. For theeconomic climate and the importance of inflation versus unemployment, onthe other hand, personal experience and observation might have beenexpected to provide significant messages. However, the empirical testingshowed that reasonable opinion time trends could be calculated withoutmodifications to account for personal experiences. In other words,personal experience messages could be ignored.

This result suggested that information in the media might colorsubstantially an individual's interpretation of personal experiences.For instance, in both the best and worst of times, people will be awareof others out of work. When the economy is considered to be good, peoplemight interpret a person's unemployment to be his own fault. However,with bad economic news, people might well shift the blame from theindividual to general conditions.

Word of mouth communication such as rumors would have been important ifthe mass media were not trusted by the population. Indeed, if governmentcontrol of the press is perceived by the public to be biased anduntrustworthy, then alternative persuasive messages will becomeimportant. Examples would range from underground publications intotalitarian states to rumors during time of war. Reliance on rumor andan underground press are probably minimal in the major democracies atthis time.

Another implication of these calculations is that successfulcalculations can frequently be made using only AP messages. The tests inthis book deliberately made the extreme simplifying approximation thatAP stories could represent all relevant persuasive messages for thetopics studied. If this approximation is valid for a large number ofdiverse topics, then the AP is indeed likely to be representative ofmost of the news in the mass media in agreement with the finding ofsimilarity in much of the mass media by Paletz and Entman (1981).Furthermore, opinion calculations will have been shown to independent,generally speaking, of special considerations for special events.Indeed, among all six examples in this book it was only necessary to addone non-AP infon once. That addition was the infon favoring more troopsbeing sent to Lebanon after the truck bombing of American Marineheadquarters and was needed to calculate the great increase in opinionjust after the truck bombing. In general, the success of the empiricaltests argues for the appropriateness of assuming that the AP couldrepresent both the print and electronic media except in very rare caseslike the truck bombing incident.

Previous investigators have frequently used the New York Times or theVanderbilt summaries of television news (e.g. Page, Shapiro and Dempsey,1985, 1987; Ostrom and Simon, 1985; MacKuen, 1981). There might havebeen minor differences between the news content in the AP and the NewYork Times or television broadcasts. However, the variations wereprobably not large.

Nevertheless, there was likely to be a significant difference in thenews stories used for this book and those identified as relevant byhuman judges. With human judges, there was probably preferentialidentification of stories concentrating on the topic under study.Stories with oblique inclusion of pertinent messages were likely to havebeen ignored. The retrievals for this book did not have this bias sincethe full texts of all dispatches in the Nexis data base were searchedusing combinations of key words chosen by the investigator (Chapter 2).All phrases relevant to the topic were identified even if they wereminor components of a story mainly discussing some other topic.

Since only text in the region of discussions of the relevant issues werecollected, the number of retrieved words per dispatch gave a good ideaof the fraction of the typical dispatch devoted to the question. Thetypical AP dispatch had 400-900 words. About 420 words were retrievedper average dispatch for troops in Lebanon consistent with theobservation that a large number of these dispatches were devoted mainlyto this topic. For the Democratic primary and Contra aid, those numberswere 310 and 258, respectively, already significantly less than a fullstory while the equivalent values for defense spending, the economicclimate and unemployment versus inflation were all under 200, meaningthat less than half of the typical story was on these topics.

This inclusion of articles mainly about other topics was appropriatesince members of the general public were frequently not looking for texton particular polled topics so thoughts were probably absorbed fromwhatever was read regardless of whether the ideas were surrounded bysimilar or dissimilar information.

The computer searches not only identified articles in which other topicswere the main focus, but such a search also guaranteed consistency.Unlike a human judge who might have been distracted when the majoremphasis was on another subject, the computer always found theprogrammed word combinations.

The thoroughness of the data base searches was seen in theidentifications ranging from 1156 dispatches for Contra aid to 12,393for the economic climate. Simply to read these numbers of articles wouldhave been a daunting task for any human investigator.

From a methodological standpoint, it is very useful that the AP alonewas able represent all persuasive messages aside from the truck bombingexample. This finding indicates that calculations can usually succeedwithout requiring ad hoc adjustments to account for special events. Theapplicability of the methods to six quite varied topics suggests thatmany future computations are also likely to be generally valid if onlyAP messages are analyzed.

6.3 Positions for which Persuasive Messages are Scored

Once the issues have been defined and the relevant persuasive messageshave been assembled, it is then necessary to obtain scores for theinfons in messages.

As noted in Appendix A, there will be overlap between the positions thatinfons are scored to favor and those that people are measured to favorin opinion polls. However, the overlap need not be complete. Forinstance, in the bandwagon scoring for the Democratic primary, therewere people with No Opinion and no infons favoring this position whilethere were infons unfavorable to Mondale and no persons polled to havethis viewpoint.

For the other cases, the overlap was much greater. For Contra aid, bothinfons and subpopulations either favored or opposed aid. In theremaining cases, there were three poll positions, ranging from oneextreme through the center to the other extreme. An example waspositions of more, same and less spending for defense. Here, infons werealso scored to favor either the same three positions or the twopositions of more and less spending. Since all messages were consideredto contain different infons, each with a content score ranging from zeroto some positive number of paragraphs, some AP dispatches were mixedmessages with positive scores for two or more infons.

For issues with two extremes and one central position, a neutral messagefavoring the central idea was distinguished from a message with twoequal components favoring the two extreme positions. In the first case,the dispatch would have had a positive score for the neutral position.In the second case, the story would have had two positive scores, one infavor of each of the extremes. Both types of scores were found andprovided a more subtle means for extracting information from messagesthan would be possible by scoring a pro and con message component asbeing equivalent to a neutral message. As argued in Chapter 2, a mixedpro and con message should not have the same persuasive effects as aneutral message.

Since dispatch scores were given in paragraphs, stories with morerelevant paragraphs had higher scores. Therefore, the dispatch contentscores were weighted in proportion to message salience.

6.4 Computer Text Scoring

Although human judging can be used to obtain position scores for APstories, this book relied on a computer method to guarantee uniformityof scoring.

Since the techniques and ramifications of the text analysis have alreadybeen presented in Chapter 3 and in Appendix C, they will not be repeatedhere. However, as an overview, there are several important novelties inthe recent computer text analysis procedures.

One feature is the decision to return to the idea of tailoring theanalyses to individual issues instead of using a generalized computercontent analysis program with a fixed dictionary and invariant scoringrules. The extra time and effort spent in making up specificdictionaries and rules were compensated by lack of need to interpret theresults, a situation opposite from that with predefined dictionaries andrules.

The method was also quite general being applicable to any text includingthose not examined for opinion formation. The possible analysis ofletters or recommendation has already been mentioned in Chapter 3. Thegenerality of the method was not at the level of the dictionary or rulesbut was rather at the level of the strategy including text filtrationsas one of the keys.

Precision in the analysis was greatly aided by the use of repeated textfiltrations to remove irrelevant text. The remaining text was morehomogeneous thereby simplifying subsequent steps and permitting the useof words which would be ambiguous in a general setting. These filtrationsteps were also useful for incorporating relationships between words inthe input text for following steps.

The procedure described above was also surprisingly robust in thatapparently important changes in the dictionaries and rules did notaffect the basic shape of the curves for predicted opinion. Thus,dispatches for defense spending could be scored either to favor more,same and less spending or just more and less spending. Also, nucleararms reduction could either be interpreted to favor less defensespending or not. A number of words in the scoring dictionaries could bechanged. Yet the basic opinion projections stayed more or less the same(FIGS. 5.3 and 5.8). Therefore, there was no need to be overly concernedthat words or rules were either omitted or misassigned during theconstructions of the dictionaries and rules.

An equally compelling argument for the robustness of the text analysiswas the fact that Fan and Swim et al. could independently arrive atdifferent dictionaries and rules for the text analysis for Contra aid(Chapter 4). The dictionaries and rules were quite different for the twoanalyses with Swim et al. using quite different strategies and assigningscores to twice as many paragraphs as Fan. Despite the differences, theresulting opinion projections were essentially the same suggesting thatthe text presented the same thoughts in a number of alternate ways.

In addition, strong conclusions could be made about the messagecomponents critical for persuading the public since the computer appliedthe dictionaries and rules blindly. This was an important advantage ofanalyzing the text using a structure where all dictionary words andrules had to be justified on logical grounds. No artificialconsiderations could be introduced for the sole purpose of fittingdesired output results.

The dictionaries and rules for the text analyses were all constructedonly by looking at the retrieved text without the application of expertknowledge specific to the topics under study. This strategy wasreasonable since the goal was to study opinion in the general publiccomprised mainly of nonexperts. The success of the calculations for sixout of six issues, despite the absence of expert knowledge, suggeststhat appropriate dictionaries and rules can be made in general bynonexperts.

Besides the omission of possibly pertinent information outside of themessages themselves, portions of the text indirectly supporting aposition were also ignored with two exceptions. One was theinterpretation of waste and fraud infons to support less defensespending, and the other was the inclusion of indirect messages in theSwim et al scoring for Contra aid. Otherwise, it was sufficient toinclude only those phrases with text directly arguing for a position,phrases analogous to the very brief extracts of film reviews inadvertisements (e.g. "best of the year . . . New York Times").

It is likely that there was usually a high correlation between indirectand direct messages since the same opinion calculations for Contra aidwere obtained from text analyses by Swim et al. using many indirectmessages and Fan scoring only those word clusters directly taking aposition on the desirability of aid (Chapter 3). The inclusion ofindirect messages meant that about twice as many paragraphs were scoredby Swim et al.

The similarities in the opinion projections using either set of scoresindicated that the message structure was the same either including orexcluding many indirect messages. The most plausible explanation is thatdirect statements advocating a position are usually coupled withjustifications citing information bearing indirectly on the issue. Inthis case, the result will be the same if either the advocacy or thejustifications are scored. A problem will only arise if statementsfavoring one position are justified while those favoring another arenot. However, given the general effort of the American mass media to usea neutral tone, a consistent bias of this sort is unlikely.

The only case including a non-AP infon was that for troops in Lebanonwhere the very unusual terrorist truck bombing of October 23 generatedan important indirect infon favoring more troops. In the case of theDemocratic primary, indirect messages due to name counts were not goodpredictorsol public opinion. Furthermore, it was quite satisfactory toignore the positions taken by the candidates on campaign issues.

The display of computer message scores as infon persuasive force curvesgives a convenient pictorial method for visualizing message structure.This is very useful for showing that information on defense waste andfraud was negligible when compared to direct statements favoring lessdefense spending (FIG. 5.12). This calculation also illustrated theability of the computations in this book to assess the relativeinfluences of different types of messages.

6.5 Ideodynamic Calculations of Opinion Time Trends

Once the issue and its positions are defined and the persuasive messagesscored, it is then possible to compute expected opinion time trends. Forthese computations, a number of approximations are made. Their validityderives from the empirical tests in this book comparing opinion timetrends with measured poll values.

One reasonable approximation is that the AP can be assigned a uniformvalidity for all stories on an issue. The validity score in ideodynamicsrefers to the credence given by public to the medium carrying themessage. The approximation for these studies was that the AP and themass media in general were highly credible and had the same highvalidity score for all stories for any one issue throughout the timeperiods of the opinion trend calculations.

A more contestable approximation was that all sources quoted in the APhave the same weight. Although ideodynamics permits different senders ofinfons to be given different weights, this weighting was not needed foracceptable time trend calculations. Instead, successful opinioncomputations could be made with the same degree of persuasivenessassigned to all infon sources including news commentators, members ofCongress and the President of the United States. In contrast, Page andShapiro (1984) and Page, Shapiro and Dempsey (1987) have suggested thatsuch weights might be useful because popular Presidents and newscommentators have greater influence. However, the data in this booksuggest that even a popular President like Ronald Reagan was lesspersuasive than his opponents for issues like defense spending andContra aid. In both these cases, a better fit to the opinion data wouldhave required that there be a lower weight for messages from Reagan andhis administration (Chapter 5). For Contra aid, infons opposing theadministration's position were more than twice as strong as infonsfavoring that position.

A methodological difference night explain the differences between theresults of this book and those of Page, Shapiro and Dempsey. Asdiscussed earlier, the manual searches used by these other investigatorsmight not have systematically included messages where only a smallfraction was relevant to the issue.

Typically, infons favoring different positions of a given issue areabout as powerful. Only for half the issues, troops in Lebanon, Contraaid and unemployment versus inflation, was it necessary to givedifferent weights for infon scores favoring different positions (Table5.3). By least squares optimization, it was shown that the maximum ratiobetween the minimum and maximum refining weights for any one of the sixissues was less than three-fold. Part of this weight was probably due todifferent degrees of effectiveness for infons supporting differentpositions. This was especially likely for Contra aid where differentscoring methods by Fan and Swim et al. both showed that infons opposingaid were twice as effective as infons favoring aid.

However, the need for different weights for different groups of infonscores might also have resulted in part from difficulties in thecomputer text analyses. No computer program, however good, can beexpected to reflect all the subtleties of natural language. Thus it waspossible that infons favoring some positions were consistently over orunder scored and that scores for some positions should actually havebeen assigned to two or more infon persuasive force functions (AppendixA). In the studies in this book, no infon scores were assigned to twodifferent persuasive force functions.

One of the most interesting conclusions is that an AP infon's audiencesize typically decreases exponentially with a persistence half-life ofone day. As discussed in Chapter 1, the persistence constant describesthe rate at which mass media infons like those from the AP lose theireffectiveness. From Chapter 5, this half-life had a consensus value ofone day. Since messages action disappeared so rapidly, it seemsreasonable that all opinion reactions were immediate. The memory ofmessages was probably only important in the very small subpopulation ofindividuals caring deeply about particular issues. For each issue, thatsubpopulation was liable to be different since any one person only hasthe time to be vitally concerned about a very small number of the issuesfor which polls are taken.

As noted in Chapter 1, the number of converts depends on an infon'scontent, validity and audience size. The strength of an individual infonat any particular time is given in a time dependent persuasive forcefunction constructed by multiplying the content score, the validityscore and the audience size at that time. Ideodynamics postulates thatthe number of people persuaded is proportional to this persuasive forcefunction.

Although not entirely expected, it was extremely useful that opinionreinforcement and message saturation can be ignored. When the populationreceives more than one infon, it is necessary in principle to considerpossible interactions among infons. As discussed in Chapter 1, suchinteractions could arise from opinion reinforcement due to theresolution of cognitive dissonance in favor of reinforcing messages.Also, interactions could lead to infons becoming less effective atsaturating densities.

For simplicity, the calculations in this book were performed by assumingthat there were not such interactions. As a result, persuasive forcefunctions for individual infons favoring a position were added to givethe net persuasive force function in favor of that position. In anatural extension of the case for individual infons, the fraction of thepopulation converted is assumed to be proportional to the persuasiveforce function for all infons favoring the position under consideration.

Ideodynamics permits the division of the population into different typesof subpopulations depending on the issue with the details of opinionchange being specified by a population conversion model detailing thepopulation conversions due to the calculated persuasive force functions.For each issue, there is a different model with the most con, non modelsinvolving people expressing opinions on a continuous scale from pro tocon. In this case, the two major variants are the sequential (defensespending, FIG. 1.2; and the economic climate, FIG. 5.32) and the directconversion (troops in Lebanon, FIG. 5.15; the name count analysis forthe Democratic primary, FIG. 5.27; and unemployment versus inflation,FIG. 5.36) models. For the more complicated bandwagon analysis for theDemocratic primary, a mixed model was employed (FIG. 5.26), and for thevery simple case of Contra aid (FIG. 5.42), the model was the sequentialand direct conversion models collapsed into one.

Usually the sequential variant was preferred whenever there was a largenumber of adherents to the intermediate position between two extremeswithout a large number of messages favoring that position. Highfrequencies for the central position could then result from trafficbetween the extremes with some persons having the intermediate opinionen route.

Besides being dependent on the persuasive force functions, the number ofconverts also increases with the sizes of the susceptible targetsubpopulations. If everyone is already convinced, there would be notarget population and no more recruits could be obtained regardless ofthe effectiveness of the messages. At the other extreme, if no onefavored the position, then the number of potential converts is large soan infon favoring the position could convert a large number of peopleeven if that was a very small fraction of the total possible recruits.

As discussed for the six examples studied, the subpopulationscorresponded to the possible responses to poll questions. The more theresponse categories, the more were the population subdivisions. Therewould have been no difficulty in dividing the population into manydifferent positions if there are many possible poll responses. However,there is always the practical limit that the sample sizes will be toosmall for accurate poll measurements if the category number becomes toolarge. For this reason, the poll data for the minor candidates werepooled for the Democratic primary.

In computing the number people converted, the constant ofproportionality is the modified persuasibility constant adjusted usingrefining weights. As discussed in Chapter 1 and Appendix A, the modifiedpersuasibility constant is the multiplier for the persuasive forcefunction and the subpopulation size in the equations for computingopinion time trends. This constant is modified by the refining weightsas will be considered below.

A number of important variables are all incorporated into the modifiedpersuasibility constant for mass media infon induced opinion change(Appendix A, Equation A.25).

First of all, the modified persuasibility constant includes the validityof the medium. As stressed in Chapter 1, the validity of a message isrestricted to that of the medium. As argued above, the rapidity of thepopulation response makes it unlikely that opinion leadership was ofgreat importance for the issues in this book. Therefore, the medium forthese studies was the mass media in general and the AP in particular.

For opinion calculations, infon validity need not have any relationshipto whether the public thinks the press is credible. The mass media needonly have a sufficient reputation that other sources of information,such as rumor, do not play an important role. If press credibility dropsso far that rumor needs to be included, then ideodynamics still shouldbe able to make reasonable predictions if the rumors can also be coded.

b. The modified persuasibility constant includes the initial audiencesize.

For mass media infons, the initial audience size refers to the number ofpeople exposed shortly after the message broadcast. The approximation inthis book was that all AP messages had the same approximate audiencesize. It was only critical for the predictions that the audience sizestay constant for each issue analyzed. The projections four any oneissue would not have suffered if the audience sizes were generally lowerfor some issues than others, even for mass media messages, since theaudience size is also incorporated into the modified persuasibilityconstant.

c. The refining weights temper the modified persuasibility constant

Chapter 1 and Appendix A note that every issue is characterized by apersuasibility constant describing the extent to which the issue isclose to the core beliefs of the population. The more central the issue,the lower is the persuasibility constant. However, it was also notedthat there could be differences in the persuasive powers of differentinfons for different target populations even for a single issue.

These differences in the persuasibility constant were modeled bymultiplying the persuasibility constant for a target population and acorresponding infon persuasive force function by an appropriate refiningweight to obtain the final constant by which persuasive force functionsand target population sizes were multiplied. Based on the studies inthis book, there were probably only small differences among thepersuasibility constants for any one issue since the ratio of thelargest to the smallest refining weights for an issue did not exceedthree-fold.

Besides minor differences in the persuasibility constant, the refiningweights also corrected for any variations in infon scoring. For example,if some infons were consistently over or under scored, then thosedifferences could be normalized using refining weights. Again, since therefining weights were not large, message misscoring was probably alsonot great.

Since the refining weights were usually not large, the opinioncalculations typically began by assuming that all infons were scoredproperly and that all persuasibility constants were the same--in otherwords that the refining weights were 1.0 for all combinations ofpersuasive forces and target populations leading to opinion conversion.

Fortunately, this simple approximation could be used for three of thesix cases studied. For the other three examples, one or two refiningweights had values different from 1.0. Deviations in the refiningweights from 1.0 were only necessary when the opinion projections forone position were consistently too high or too low.

Leaving refining weights and returning to the modified persuasibilityconstants, it should be noted that these constants should have changedwith the issue while the infons' validities, initial audience sizes, andpersistence constant should have stayed the same for all AP dispatchesacross all topics.

Since the modified persuasibility constants varied from 0.09 to 7 per APparagraph per day, the superficial interpretation is that the public canbe much more easily persuaded for some topics than others. The value of0.09 was for the economic climate study where the number of articles wasover 12,000 with most mentioning the issue only in passing. The greatestease for changing public opinion was for infons favoring inflation beingimportant for the issue of unemployment versus inflation (7 per APparagraph per day).

It is perhaps premature to draw firm conclusions from the modifiedpersuasibility constant. It would be dangerous to say that the low valuefor the economic climate actually meant that people were very fixed intheir opinion for this question so that many more paragraphs were neededto cause a change of mind. It is possible that people did not notice therelevant paragraphs in articles on the economic climate because theirminds were on other portions of the article. Also, the persuasivestrength of a paragraph on the economic climate might have been weakerthan one for unemployment versus inflation.

There was also the stochastic problem of having sampled differentamounts of text for the six examples. Comparison of FIGS. 5.3, 5.5 and5.6 shows that the fluctuations in the projections increased as fewerdispatches were included in the analysis. Similarly, the predictionsbecame worse as the modified persuasibility constant increased due, notto the inherent suitability of higher values for the constant, but to alimited sample of messages giving such large fluctuations thatpredictions deteriorated. Therefore, all the modified persuasibilityconstants might be underestimates with the problem being most severe forthe economic climate where only 3.7% of the identified dispatches wereretrieved and least severe for unemployment versus inflation where 99%of all stories were collected.

Yet another complication was the approximation (Appendix A, EquationsA.21 and A.22) that all relevant dispatches were identified during theNexis search. If a significant number were left out, the interpretationsof the modified persuasibility constants would also need to be altered.

The incorporation of a number of different constants in the modifiedpersuasibility constant and in the refining weights is both an advantageand a disadvantage. The advantage is that only one small set ofconstants is needed for accurate opinion predictions. The disadvantageis that the constants incorporate not only audience persuasibility butalso audience size and validity of the AP as well as possible infonmisscoring. As a result, it is difficult to dissect the contributions ofthe individual factors.

The relative constancy of the modified persuasibility constants for anyone issue over time also meant that the parameters changed only slowlyif at all with time although it was possible that the constants valid inearly times were less appropriate at later times when studies werecontinued for as long as 9 years (defense spending, FIG. 5.10). When therefining weights were the same for all positions, the suggestion wasthat it was as easy to sway opponents as proponents of an idea giveninfons worth the same number of paragraphs.

6.6 Insensitivity of Predictions to the Starting Opinion Values

One reassuring observation was the convergence of opinion calculationsto that dictated by the message structure regardless of the initialopinions used for starting the computations. Therefore, errors in thefirst opinion time point used for the computation were not important.For example, the projection assuming a population with everybodyfavoring more defense spending (FIG. 5.7) showed that even this veryinaccurate initial poll point still led to the same projections after adelay. Consequently, if initial poll values only have small errors, theprojections would home very rapidly to the proper values.

There is another illustration of this point in FIG. 5.20 for troops inLebanon. In this case, projections were made both with and without thetruck bombing infon in favor of more troops. Again, the wide disparitiesseen just after Oct. 23, 1983 disappeared within two to three months.

6.7 Interpretations for All Ideodynamic Parameters

A major strength of ideodynamics is the fact that it was deriveddeductively from known phenomena. As a result, all the constantparameters in the model were interpretable in terms of socialinteractions. There was no appeal to arbitrary parameters only added tofit the data. The ready explanation of all constants demonstrates thatideodynamics does not impose an arbitrary mathematical structureinappropriate for the analysis of opinion formation.

In summary, these parameters for mass media messages are:

(1) The persistence constant describing the exponential loss in theiravailability to the population and having a universal half-life of oneday.

(2) The modified persuasibility constant including the validity of themedium, the size of the typical mass media infon (when only one mediumlike the AP is used), and the malleability of the population for theissue dependent on the closeness of the issue to the core beliefs of thepublic.

(3) The refining weights, one for each conversion in the populationconversion model. The weights describe the extent to which the infonsfavoring one position are more or less powerful than infons favoringother positions and the extent to which messages may have beensystematically misscored so that unusually high or low content scoresmay have been Given to infons favoring one or more positions.

6.8 Significance of No Opinion Change

The fact that opinion will reach that dictated by the informationstructure is of interest in cases where little opinion change ispredicted and little is observed. Relatively speaking, that was the casefor Contra aid and for the Democratic primary where the changes were notnearly as large as for the other examples.

Using the bandwagon analysis for the Democratic primary, the observedvalues were the predicted ones. With the name count scoring only, theprojections were for a steady decline in opinion supporting Glenn andMondale with a corresponding rise in those favoring the others until thefinal level of about 50% was obtained for the others. Indeed, that wasthe percent name count for this group (Table 5.2). The name countanalysis illustrates how an inappropriate information structure willlead to a prediction worse than that of no opinion change because thepoll values will be projected to change away from that actually found tothat dictated by the erroneous information.

6.9 Analysis of Persuasive Messages Acting on Public Opinion

The ability of ideodynamics to relate percentage values for publicopinion with information available to the public, in a time dependentfashion, has obvious practical applications.

It is useful to recall that the calculations were divided into twoparts. In the first, messages were scored by computer for their supportof individual positions or ideas. In the second, the scores were enteredinto the ideodynamic equations for calculation of opinion after aninitial poll date. It is necessary to stress the two step nature of theprocess because it is possible to enter message scores from alternativemethods into the opinion projections. It would be quite appropriate forhuman judges, for example, to assess information instead of using acomputer program. Such a procedure might be appropriate for calculatingpublic responses to advertising campaigns in which manufacturers wish toimprove their market share. This would be especially true if theadvertisements were more complex than text, including other visual andaural cues.

It has already been mentioned that the computer text analysis has theadvantages of being robust, easy to use, and capable of giving accurateopinion projections. These same comments apply to opinion calculationsusing infon scores.

Among the important advantages of the ideodynamic opinion calculationsis the paucity of parameters. In the simplest cases, it is onlynecessary to optimize the modified persuasibility constant if theuniversal persistence half-life is used and if the refining weights areall 1.0. Since this simplest case was sufficient for three of the sixsamples of this book, the modified persuasibility constant is probablythe only parameter which will need to be optimized for a significantfraction of all cases. The scarcity of parameters means that they can beset using very few measured poll values.

CHAPTER 7 Significance of Work to Theories of Opinion Formation

In discussing the ramifications of the studies in this book, theargument is made that the approximations for the computations areplausible because empirical tests with six diverse sets of data yieldedreasonable opinion time courses in all cases (Chapter 5). If additionalexamples confirm the methods, then the theory will be on sounder footingstill.

In a recent and thorough review of the persuasion literature with anemphasis on time trends, Neuman (1987) did not find any other reports inwhich accurate time courses of opinion could be computed from mass mediamessages. Furthermore, as noted in Chapter 2, ideodynamics does haveunique features. For example, this model can be used to compute anopinion time series using arbitrarily small time intervals such as the 6or 24 hours in this book. This use of very short time intervals has notbeen reported in the past and demonstrates that mass media messages havea duration of less than a week. The small intervals of calculation alsoprovide enough precision to study very rapid increases and decreases inopinion as was found for troops in Lebanon (Chapter 5). Therefore, thisand other unusual features such as the ability to explore opinionreinforcement mathematically means that no other models have beenreported with the same powers as ideodynamics. In consequence, furtherempirical tests can point to limitations of ideodynamics but will notnecessarily support competing models for the responses of opinion toinformation since none have been reported with the same capabilities.

In addition to providing the empirical evidence for opinion beingcalculable from persuasive messages, the studies in this book also giveinsights into the processes of persuasion as discussed below.

7.1 Mass Media Messages and Opinion Leadership

Ideodynamics can be adapted to model all news reports first persuadingopinion leaders who then rebroadcast the messages and influenced thepublic at large (Chapter 2). Such a two-step process would have hadthree important effects: (1) The mass media messages would haveamplified audience sizes and credibilities. (2) The effect of mass mediamessages on the public as a whole would have been delayed as the opinionleaders absorbed and retransmitted news. (3) Continued rebroadcast bythe opinion leaders would also have given messages an apparently longerduration than if they had acted on the population directly.

Data in this book on the last two points suggest that opinion leadershipof this type is unlikely. The first line of evidence is that the lagbetween media messages and opinion response was at most a few hourssince opinion in favor of more troops in Lebanon increased from under 7%to over 30% within two days after news of the truck bombing of AmericanMarine headquarters in Lebanon in 1983 (FIG. 5.22). That would gaveopinion leaders to little time to process and retransmit messagesfavoring more troops in any reasonable interpretation of the two stepprocess process of persuasion.

The second set of data arguing against this sort of opinion leadershipis related to the duration of mass media messages. As discussed inChapter 5, least squares optimizations for five of the six examples gavea one day persistence half-life for AP messages. The exception was forContra aid where it was inherently difficult to perform a validoptimization since there was little change in either opinion or themessage structure. Therefore, a one day half-life is a good consensusvalue. From a practical standpoint, this is advantageous because a oneday half-life means that infons lose their effect within about a week.Therefore, opinion calculations only need include information within aweek or two before the beginning of the computation. This persistencehalf-life is substantially shorter than the small number of months usedby other investigators (e.g. Hibbs, 1979; Kernell, 1978; Mueller, 1970;Ostrom and Simon, 1985; and Zielske and Henry, 1980). Therefore, it isimportant that this consensus half-life was assigned after least squaresoptimizations for six independent issues.

This short duration of messages means that long term memory is probablyrelatively unimportant, so far as persuasion of the public as a whole isconcerned, for the issues studied. The majority of the public are casualobservers for most polled issues and will probably either undergo mindchanges either rapidly, or not be persuaded at all, after becomingacquainted with a news item. If their opinion stays the same, they willsoon forget messages as new ones arrives to take their places in ourmodern society where information is saturating. Perhaps, in a societywith fewer new messages, old information might have a longer effectsince they will be displaced in a slower fashion. Obviously, there willbe a few experts in the populations who will remember old messages anduse them for decision making. However, they will only comprise a tinyfraction of the people polled.

In brief, both the rapidity of the public response and the short messageduration arue against a two step process of persuasion working throughopinion leader.

Since the concept of opinion leadership was formulated from survey datawhere the respondent was asked to reconstruct his information sources(beginning with Lazarsfeld, Berelson, and Gaudet, 1944), it would beinteresting to perform a survey asking people for the reasons theychanged opinions for the issues in this book. It is entirely possiblethat the result would be somewhat different than that suggested by themeasurements here studying actual media messages with their actualbroadcast times. In fact, memory is often significantly distorted(Markus, 1986) with people misrecalling prior attitudes. Theunreliability of memory is further demonstrated in Menzel's (1957) studyshowing that physicians are likely to remember that they beganprescribing an antibiotic at a time significantly earlier than the timethey actually did so. When challenged to give the reasons for an opinionchange, the typical person may well feel obliged to provide a morecoherent reason and sequence of events than actually occurred.

The unimportance of opinion leadership has important consequences forpersons interested in generating persuasive messages. The implicationfor efforts to change public opinion directly is that the messagesshould actually be directed at the populace and not at some mythicalgroups of elites who will, in turn, convince the public as whole.However, from a propaganda or public relations standpoint, it may indeedbe useful to try first to convince other elites who will broadcastadditional mass media messages favoring the propagandist. These elitesmight indeed have inordinate influences on the public, but thatinfluence is most likely to be due to their access to mass media ratherthan to their ability to persuade followers directly after receivingmedia broadcast in a two step process.

It should be realized, however, that this direct importance of the massmedia is likely to be restricted to national issues where the mediadiscuss the issues extensively and where local leaders are unlikely tohave access to privileged information. For local issues, not subject towide media debate, it is quite possible that the local elites can havemore of a monopoly on privileged information and can have significantinfluences on their followers without use of the mass media.

7.2 Reinforcing Role of Persuasive Messages

In Chapter 1 and Appendix A, there is an extensive discussion of how thereinforcing role of persuasive messages can be entered into theconstruction of the persuasive force curves. A refinement was furtherproposed for the dulling effects due to frequent repetition ofpersuasive messages. Fortunately, it was unnecessary to invoke eitherphenomenon for the studies in this book.

The finding that opinion reinforcement can be ignored is not incontradiction with previous data from surveys in which people are askedabout their impressions of persuasive messages (e.g. Lazarsfeld,Berelson, and Gaudet, 1944; Trenaman and McQuail, 1961; Noelle-Neumann,1973, 1977, 1984). A person can say in a survey that his opinion wasreinforced without such reinforcement actually taking place.Ideodynamics argues that it makes no difference if a person claimsopinion reinforcement so long as his mind is actually as easy to changeas that of another person not subject to reinforcing information. Ifthere is no difference in persuasion, then the ideodynamicinterpretation is that the subject was fooling himself into thinkingthat his views were being strengthened.

Having just noted that time-dependent reinforcement was not observed, itis still formally possible that there was a constant level of timeindependent reinforcement due to favorable infons. In the calculationsin this book, constant reinforcement cannot be distinguished fromgenerally lowered scores for infons. However, when the actual infonsfavoring different positions were examined (Chapter 5), it is clear thatinfons favoring individual positions were not always present at the samelevels. Furthermore, the differences in information were sufficientlylarge that public opinion did shift. Since it seems rather forced toargue that reinforcement is constant while information favoring changeis not, the most plausible conclusion is that reinforcement is usuallyrelatively unimportant-when measured by its effects on opinion change.

One theoretically possible explanation for the small amount ofreinforcement observed is that information densities were so low thatmost people only received one unmixed message. Under these conditions,it would make a difference if most messages were mixed, containinginfons favoring more than one position, or if most messages were purehaving only one infon with a non-zero content score. With mainly puremessages, it is conceivable that reinforcement could be very large butinvisible in the ideodynamic computations since people receivingreinforcing infons would not have received any infons persuading them tochange their minds. Similarly, people exposed to infons capable ofconverting their opinions would not have been exposed to reinforcinginfons. Therefore, reinforcement can be very strong but irrelevantbecause people whose minds are being changed are not subject to thereinforcing infons.

This explanation is unlikely, however, since a significant number of theactual messages were mixed. The sample text analyzed in Appendix C, forexample, supported three positions. Any person exposed to infonsfavoring one of these positions is likely to come in contact with infonsfavoring the other positions as well.

A distinction must also be made between time dependent and timeindependent reinforcement. If reinforcement is constant with time, thenit will be invisible in ideodynamics. The effects will be absorbed intothe persuasibility constant. The critical point for the calculations inthis book is that reinforcement should not be higher at some times thanothers due to changes in the levels of reinforcing information.

Besides ignoring reinforcement, the satisfactory calculations in thisbook also did not need adjustments for information overload. The endresult was the mathematically agreeable conclusion that persuasive forcecurves could be constructed simply by adding the forces for individualinfons.

7.3 Cumulative Effects of Information Rather than Minimal Effects of theMedia

Having just discussed reinforcement, it is appropriate to comment on thelong standing, but not undisputed, concept of the "minimal effects" ofthe media (Cook, Tyler, Goetz, Gordon, Protess, Leff and Molotch, 1983;Erbring, Goldenberg and Miller, 1980; Funkhouser, 1973a and 1973b;Iyengar and Kinder, 1986; Iyengar, et al., 1982, 1984; MacKuen, 1981 and1984; Page and coauthors in a series of papers with Page, Shapiro andDempsey, 1987 being the most recent; Patterson and McClure, 1976;Robinson, 1976; Rogers, 1983; Shaw and McCombs, 1977; Wagner, 1983). Oneinterpretation is that the media have minimal consequences on opinionchange because the maximal consequence is reinforcement. As noted in theprevious section, reinforcement was not detected for the examples inthis book. However, reinforcement may occur in other circumstances.

Nevertheless, under any circumstance, the essential question from thestandpoint of social change is whether reinforcement is so strong thatno change occurs at all. For issues where opinions do change,reinforcement cannot be so overwhelming as to block all movement. Forthese issues, the crucial element in determining public opinion is theresidual amount of persuasive force, however small, which can overridethe reinforcing information since those are the effects which will causethe social alterations.

Another interpretation of the law of minimal effects is that mass mediamessages are small relative to other messages. To draw this conclusionit is necessary that all relevant messages favoring all positions beincluded in the model, and that is one of the principal features ofideodynamics. For instance, ideodynamics can show that certain messagesmight have apparently minimal effects because the opposing messages areoverwhelming rather than the favorable messages being ineffectual. Withfewer opposing messages, the favorable communications might be quitepersuasive. Due to this importance of preexisting and othercontemporaneous messages favoring all positions, it is difficult toassess the importance of natural messages such as those from aPresidential debate on voter preference (e.g. Katz and Feldman, 1962;Mueller, 1970b; Sears and Chaffee, 1979) unless all other importantmessages relevant to the campaign are also entered into thecalculations. The strength of a message will appear "minimal" if thereare many other relevant messages supporting the same position. On theother hand, if there are few other messages of this type, the identicalmessage can seem to have a "maximal" effect.

Both possibilities were graphically demonstrated for the case of troopsin Lebanon. In the absence of the truck bombing infon supporting moretroops, the opinion projection was poor with a large MSD (FIG. 5.19,lowest frame) and the opinion projection entirely missed the increase inpeople favoring more troops (FIG. 5.21). When 80 AP paragraphs favoringthis position--equivalent to about 10% of all AP paragraphsanalyzed--were added on Oct. 23, 1983, there was a dramatic improvementin the MSD which decreased 8-fold. The effect was obvious uponinspection of the projected time trends (FIGS. 5.22 and 5.23). Thisresult was expected because there was very little other informationfavoring more troops at any time. With a low background, there was adramatic and "maximal" effect when the persuasive force functionfavoring more troops included these 80 paragraphs.

The situation was quite different for the addition of a truck bombinginfon favoring troop withdrawal with a strength anywhere in the entirerange from 0 to about 40 AP paragraphs. Such an addition had essentiallyno effect on the fit as seen by least squares optimization (FIG. 5.20,bottom frame). This was again expected since there was a large amount ofadditional information in this direction even in the absence of thistruck bombing infon (FIG. 5.24). Therefore, even a very large amount ofadditional information could have a "minimal" effect on opinioncalculations.

Another example of the minimal effects of the media was the small effectof stories on defense waste and corruption on opinion favoring lessdefense spending (FIG. 5.15). This did not say that such stories wereinherently negligible but only that their effects were small in thetotal sea of infons in that direction. With fewer other infons directlyfavoring less spending, the waste and fraud stories would have been muchmore important.

Therefore, individual mass media messages or groups of infons can appearto have minimal or maximal effects depending on the prevalence of otherinformation, both pro and con. The example with Lebanon has shown howminimal and maximal effects can be seen even when all the relevantinformation is restricted to the mass media.

Although the mass media is likely to be the major source of persuasivemessages for some issues, ideodynamics also can include importantamounts of information from non-mass media sources if those messages canbe coded as infons. In fact these other messages might sometimes evenswamp out the effects of mass media infons. An illustration might be theadoption of antibiotic usage which was correlated with physicianinteractions and not media messages (Coleman, Katz and Menzel, 1966).Mass media messages were not likely to have contained all the technicalinformation a doctor would have desired before making such aprofessional judgement. For this information a doctor was more likely toseek advice from knowledgeable colleagues. In this case, the mass mediamight have had a minimal effect due to the primacy of messages of allthe water. In the same way, as stated at the beginning of this book, itis more useful to think a series of persuasive messages having apowerful cumulative effect rather than individual messages havingminimal effects. Therefore, the law of the minimal effects might be morefruitfully replaced by the concept of the cumulative effects ofinformation.

7.4 Caveats for Laboratory Experiments

The necessity of taking into account all persuasive messages acting onthe population also points to an important caveat in evaluating theresults of studies where the population is measured just before andafter an intervention with persuasive messages. Such studies mightinvolve individuals being exposed to messages such as television news ina laboratory setting with measurements on attitude being made before andafter the treatment (e.g. Iyengar, et al., 1982; Iyengar and Kinder,1986). Without knowing the informational residues from relevant messagesreceived before the laboratory intervention, it is not easy to predictwhether the added messages will have a large or small effect as justdiscussed. Ideodynamics also notes that opinion can continue to changeafter message exposure within the concerned and motivated subpopulationinfluenced by remembered information. Although though this effect wasnot large for the public at large not concentrating on the issue,remembered information may play a more crucial role in a laboratorysetting where the subjects might devote concentrated thought on anissue. Therefore, different opinion changes might be found at differenttimes after the time of information exposure. A single time point mightgive a misleading result.

7.5 Law of the 24-Hour Day

At this point, it might be useful to mention again the law of the24-hour day discussed earlier in the Introduction and in Chapter 1. Thislaw simply acknowledges that time moves inexorably forward leaving mostpeople with insufficient time to consider most issues in depth. As aresult, the public at large is likely to make most decisions based onlyon superficial information. This unavoidable superficiality is probablycentral to the success of ideodynamics and stresses the fact that ananalysis of the passage of real time is crucial to any understanding ofmass behavior.

The law of the 24-hour day means that caution must be exercised ininterpreting data from one shot surveys where people are asked toreconstruct the events and reasons involved in decision making (Lane andSears, 1964; Robinson and Clancey, 1984). The original decision may wellhave been made without much reasoning and have been based largely oninfons merely advocating a position. However, a person asked toreconstruct events is likely to try to find a logical explanation asdiscussed above.

It is argued in the Introduction and in Chapter 1 that there is acertain inevitability in the superficiality with which the bulk of thepopulation will absorb information about an issue. Indeed, the greaterthe diversity of decisions made by an individual, the greater will bethe variety of relevant information. As a result, the less will be thecare with which individual decisions can be made. This consequence isseen at the level of the public as a whole, leading to the ability touse ideodynamics to predict opinion from the structure of persuasivemessages.

However, the same arguments of time limitation will apply to thegoverning, business and other elites within the population. Bydefinition, these people are the ones with the most power. The morepowerful the person, the broader the range of responsibility and themore superficial must be the decision making due to time constraints.These comments stress the importance of having competent a competentstaff which can subdivide the responsibility so that differentindividuals have small enough domains that they have the time to assessthe primary information with some care.

7.6 Interpretations of Ideodynamic Parameters

As discussed in Chapter 6, the three types of ideodynamic parameters formass media messages are the persistence constant, the modifiedpersuasibility constant and the refining weights. The relationshipbetween the persistence constant and opinion leadership is considered atthe beginning of this chapter. The modified persuasibility constant isdiscussed at length in Chapter 6. The refining weights also haveinteresting interpretations.

These weights reflect, in part, corrections needed to account forsystematic over or under scoring of certain classes of infons (Chapter6). One example might be the case of unemployment versus inflation.Here, it was necessary to reduce the content scores for infons favoringthe equal importance of unemployment and inflation to half of the valuefor infons supporting the importance of unemployment. At the same time,infons stressing the importance of inflation needed to be augmented by140%. It was not easy to design the text analysis for this issue so thatinfons scored for the equal importance of unemployment and inflationexactly favored equal importance. It was quite conceivable that asignificant portion of these infon scores actually had a consistentlyhigher component supporting the importance of inflation. If a portion ofthe infon scores favoring equal importance were transferred to theinflation important position, all refining weights could actually havebeen the same, consistent with equal malleability of the public for allpositions of the issue. Therefore, for unemployment versus inflation,the refining weights might have mainly reflected scoring errors.

If unemployment versus inflation also can be characterized by the samerefining weight for all positions once the messages have been scoredproperly, the use of a common refining weight may be the rule ratherthan the exception since it would have applied to four of the sixexamples tested. Besides unemployment versus inflation, this commonalitywas also seen for defense spending, the Democratic primary and theeconomic climate (Table 5.3). Therefore, identity in refining weightsmay be the rule rather than the exception.

This situation would correspond to strength of feeling being correlatedmore tightly with the issue than its positions. As the issue comescloser and closer to the population's system of beliefs, thepersuasibility constant will decrease and the population will be moreand more refractory to messages with opposing ideas. However, once aperson has adopted a new position for this issue, any position, he willbe as difficult to dislodge. For core beliefs, it may be very difficultto change any opinion. Again, once changed, it would be as difficult tocause a reversion to the original position--if the refining weights arethe same for the two conversions.

The ability to dispense with refining weights for three or four of thesix positions has the aesthetically pleasing result of reducing thenumber of parameters needed in the calculations. Therefore, whenever achoice is available, it seems desirable to adjust the analysis so thatrefining weights are not used. This was an important reason that thename count analysis for the Democratic primary was considered inferiorto the bandwagon analysis (Chapter 5). The poll points could have beenfit much better than shown in FIG. 5.30 for the name count analysis ifrefining weights different from 1.0 were permitted. That would havemeant giving Mondale a much higher refining weight than that for theOthers while the refining weight for Glenn would have stayed the same.

Although interpretations might be given for why those weights arereasonable for the name count analysis, no variations at all wererequired for the bandwagon analysis. The dispensibility of refiningweights for the bandwagon analysis means that this analysis willprobably be more useful for future predictions for the popularity ofpolitical candidates since no weights need to be optimized. If thebandwagon analysis continues to be better than the name count analysis,the implication would be that people do have the sophistication to lookbeyond mere names for their candidate preferences--when the names becomeestablished. However, the additional implication is that people may notlook beyond bandwagon words to the candidates' positions or activities.

In general, if two competing analyses require different numbers ofparameters, the most powerful model will be the one with the fewest sothis criterion of avoiding additional parameters will be used to chooseamong different ideodynamic analyses whenever possible.

In contrast to the case for unemployment versus inflation, it isunlikely that the only problem for the Contra aid issue was infonmisscoring because both Fan and Swim et al found that infons favoringfewer troops were more than twice as effective as infons favoring moretroops. Two, rather different, computer scoring schemes both led to thisconclusion. Similarly, infons favoring less troops in Lebanon also werefound to be 1.6 times as powerful as messages favoring more troops ortroop retention at the same levels. Therefore, if the scoring for troopsin Lebanon is also reliable, there is the hint of the generalizationthat the American public has certain isolationist tendencies leading toa reticence in foreign adventurism regardless of whether the locale isNicaragua or Lebanon.

Given only two issues, this thought must be considered just a suspicion.However, consistent differences in the refining weights for more foreignpolicy issues might solidify the hypothesis. This analysis illustrateshow important differences in refining weights might give significantinsights into differences in the response of the population to differenttypes of information.

7.7 Nature of Effective Persuasive Messages in the Mass Media

The successful applications of ideodynamics give insights into theimportant aspects of persuasive messages. For instance, thesuperficiality forced by multiple demands on limited time is probablyalso at the heart of the success of the big lie in propaganda wheretotally implausible assertions are made baldly and with no apologies.These lies will be believed since most of the population will bepreoccupied with other concerns and will not take the time to pause andreflect carefully on the situation.

However, in order for the big lie to work, the medium transmitting thepropaganda must have a good reputation because the populace will assignvalidity to the big lie in proportion to the reputation of the medium.The importance of the credibility of the medium was recognized even inearly fairy tales such as the one about the little boy who cried wolf.

From time to time, however, the public can check the reliability of anymedium against other information in the same way that the villagers wereable to assess the validity of the wolf alarms. If the medium showssigns of inaccuracy or unbelievability based on the public's comparisonswith other information, then the reputation will fall and few reports bythe medium, regardless of topic, will be believed.

Although the media cannot afford to tell lies and retain credibility,the press still has very broad latitude in the choice of messagesdisseminated. Members of the public usually will not fault the media foromitting items because they realize that the press cannot transmit allthe information received so some items must be omitted.

The press might even be able to retain its trust in cases where thepublic might like to know about a suppressed item, so long as reportedstories are accurate and the press or its censors can efficientlypropagate the idea that the omitted item is one which is too delicatefor transmittal to the public at large. One possible rationale isnational security. However, the censors must be careful to suppress onlythose items which the public will forgive the press for suppressing.This may mean that the censorship should be accompanied by simultaneousmessages training the public to tolerate the removal of certain items.Such tolerance is seen by public acceptance of censorship duringwartime. Furthermore, the public will sometimes even favor overtmanipulation of the available information. This is usually true in thepublic providing unabashed support for public health and anti-drug abusecampaigns. It is the rare person who will insist on the publication ofbalanced stories presenting both the disadvantages of drug use and theglories of transient highs.

This analysis shows that, a credible medium can transmit the big lie inthe short term, and the law of the 24-hour day can prevent closescrutiny Of medium content. However, in the long term, such a strategyhas the problem that all which is said, even if true, will be suspectonce credibility is destroyed.

Public distrust will be important as soon as the population resorts toalternative media. Ideodynamics asserts that the effects will be minorif a medium loses credibility but is still the only source of pertinentinformation. In this case, the medium will be able to influence thepublic more slowly but will still move the public in the directionsspecified by the media messages. If, the public resorts to other mediasuch as rumor and an underground press, however, then the effects of theoriginal medium can be drastically decreased.

In brief, then, the mass media together comprise a critical instrumentin a modern democracy. As discussed above, the media are probablycrucial for shaping both the agenda and opinion within the agenda for alarge number of issues. However, the analysis may be misleading unlessit includes all information bearing on the issue. In the same way, atugboat pulling an ocean liner to port can have maximal effects in calmseas but minimal effects in a hurricane where the non-tugboat forces onthe liner are overpowering. It is only by considering the cumulativeeffects of all messages relevant to an issue that the true impact of themedia can be established.

One of the most sobering aspects of the importance of the mass media isthe small number of people involved in determining press contents.Indeed, the concepts of press scoops and exclusivity explicitlyacknowledge that a single reporter or small group of reporters for onenews organization can have an unusually large effect on the newsstructure.

The small number of people involved is related to the limited number ofjournalists covering any given issue. Most local press organizations,both electronic and written, do not have the resources to cover nationaland international events. For these events, most reports in the pressare second hand, derived in large part from wire services like theAssociated Press. This press agency in turn will assign only a fewreporters to any one story. These persons then have the capacity--andobligation due to limitations of time and space--to select what theyfeel is newsworthy. As a result, news from Journalists, howeverunbiased, is still colored by the inevitable omissions made.

In addition to the reporters are the editors who also exercise the powerof agenda and set, together with the journalist, the tone of mass mediamessages. Although editors at the Associated Press and local newsorganizations can both remove information, they will not be able to addmuch unless they have access to other news sources. Certainly, therewill be a few alternatives such as reports from other newspapers, wireservices and television. But, again, the numbers of alternative sourcesare not many with each of these sources only having a few primary newsgatherers. In fact, newspapers typically give prominent treatment tostories identified by the wire services as important (Chapter 3),thereby ceding even more power to the wire services. Since a limitednumber of journalists and wire service editors are responsible for muchof the original news, the public receives its news from a ratherrestricted group of individuals for any one issue.

The ideodynamic studies in this book also emphasize the need forcontinual responses to the opposition. The very malleability of publicopinion as describe above means that opinions are not permanent so it isalways necessary to keep an eye on the activities of the opponents.There are no permanent victories or defeats. If the opponents generatemore information, then the proponents must do likewise. As seen fordefense spending (Chapter 5), the increase in public opinion in 1979favoring more spending (FIG. 5.6) corresponded to increased informationin this direction without important changes in the messages favoringless spending (FIGS. 5.4). Then, opinion was brought back down not by adecrease in messages favoring more spending but by an increase inmessages favoring less spending. The total decibel level of the debatewas therefore higher from 1982-1984 than from 1977-1979 even thoughpublic opinion about defense spending was about the same in both timeperiods.

By multiplying Persuasive force curves by the sizes of the relevanttarget populations, ideodynamics recognizes that mixed messages withstrong unfavorable components can be favorable. For example, largelyunfavorable information can have a significant positive effect when mostof the population is in the opposition. Obviously, if only a minisculeamount of favorable information is included together with theunfavorable then only a tiny fraction of the opposition will beconverted. However, if everybody is in the opposition, then a smallfraction of a large number can still be sizable in terms of absolutenumbers. This is one key to the observation that name mention for aproduct or political candidate, even if it is unfavorable, can stilllead to increased purchases or popularity. However, that strategy mustbe abandoned as soon as the product or candidate gathers a large numberof supporters because the negative publicity will then cause a loss inloyalty.

Similarly, a terrorist can expect to gain favor for his position if theterrorized population is overwhelmingly opposed to his viewpoint. Hewill not lose a significant number of sympathizers since he had few tobegin with. But if even a small mention of his cause is included in thenews, that might be able to convert a small number of opponents andhence give the terrorist a significant, but small number ofsympathizers. Therefore, at low sympathy, terrorism can indeed beeffective for recruiting converts to the terrorist's cause. In fact, thetool of terrorism can be made more effective by condemnations which onlyserve to highlight that cause.

This gain to the terrorist will be significantly greater if the news isneutral with a sizable component mentioning the terrorist's cause ratherthan being violently anti-terrorist with a very small pro-terroristcomponent. This analysis follows from the argument (Chapter 2) thatevenly mixed messages are not neutral unless the population is splitfifty-fifty on an issue.

As soon as the terrorist has an appreciable number of supporters,however, he should shift his tactics so that his actions do not drivehis adherents to the other camp. He should then behave more responsibly.By then he can probably afford to, because he is also likely to haveeasier access to the media.

7.8 Cause and Effect

Ideodynamics claims that opinion can be computed from an analysis of thecontent, reputation and timing of the messages arriving at thepopulation. One alternative is to postulate that messages reflectopinion. Of these two Possibilities, the studies in this book suggestthat the more powerful and general model involves opinion followingmessages because this phenomenon was observed for all six cases. Thereverse concept of messages tracking opinion clearly cannot always betrue. Some messages are clearly event driven. For example, the truckbombing in Lebanon was a newsworthy event which could not have beenpredicted from the opinion structure. Also, there was no way to predictfrom opinion on defense spending that net information favoring morespending was going to increase in 1979 and decrease in 1981.

The relationships between message generation and impact might beexplained by postulating that opinion always reflects messages whilemessages reflect both opinion and unusual events. Therefore, in theabsence of extraordinary news, it might be possible to model bothmessage generation and message impact by exploring the relationshipsbetween the two. This modeling can be performed in the context ofideodynamics which has a structure for separating information generationfrom information impact.

At the core of this structure are infons which code essential featuresof persuasive messages. This book has already examined the impact ofinfons. To extend the model, it is only necessary to postulatemathematical models for how opinion affects infon generation. Persuasiveforce functions can then be computed based on the expected infons. Usingthese functions, it will be possible to compute both opinion frommessages and messages from opinion to give a time trajectory of opinionwithout any need to measure messages directly.

The calculation could proceed by starting with a set of opinion valuesat a particular time. If the opinion structure causes certain messagesto be generated, then those messages should be computable since thestarting opinion structure will have been specified. The new messagescan then be used to predict the opinion structure at the next timeinterval. The resulting opinion can then be used to calculate themessages in that time interval. These messages and the calculatedopinion for that time interval can be used to compute opinion in thefollowing time interval. This process can be repeated ad infinitum tocalculate a final time trend independent of actual message measurements.

As discussed in Chapter 2, this has already been done for the case whereinformation favorable to an idea is broadcast in proportion to thenumber of believers and where there is no opposition to the idea. Inthis case, ideodynamics predicts that the idea will increase in alogistic fashion until the entire population accepts the idea (Fan,1985a) as has been found for the adoption of many innovations (Hamblin,Jacobsen and Miller, 1973). On the other hand, if proponents andopponents have the same powers of message emission, then their ratiosshould not change, regardless of what those ratios are, even though bothgroups may increase in size (Fan, 1985a).

In future studies, other models will be explored including those toexplain how fads can come and go with great rapidity. Such models mightrequire a boredom effect where the adopters of a fad diminish theirmessage broadcast as the time proceeds. Similarly, there might be asocial pressure effect due to people observing others with an opinion,fad or habit. In this case, the more the people with the trait, thegreater would be the social pressure. The result would be socialpressure infons which have already been incorporated into messagegeneration models for habits like smoking (Fan, 1985b).

For habits, there might also be a recidivism effect due to people whohave just changed a habit being momentarily euphoric at having made thechange and then being driven back to the old habit by nostalgia. Part ofthis nostalgia might be biological reflecting a desire to return to thephysiological state of an addiction. This recidivism effect could beentirely due to personal experiences in which case the ideodynamicequations would include personal experience infons due both to euphoriaand nostalgia (see Fan, 1985b for equations).

It is also possible that people whose ideas are in the ascendancy willbe more vociferous in their dissemination of favorable messages as hasbeen proposed by Noelle-Neumann (1984) in The Spiral of Silence. Such aphenomenon might be modeled by persons being more willing to sendfavorable messages when they perceive that their position is gainingprogressively more converts. Unfortunately, Noelle-Neumann (1984) doesnot propose how the spiral of silence ends so the model will probablywill lead to the position in ascendancy gradually becoming the onlyacceptable position, a circumstance which would be inaccurate for socialissues where controversy does not die completely.

For the study with the Democratic primary in this book, the mostsuccessful analysis used bandwagon words assuming that people will beswayed simply by news that a candidate's campaign was proceeding well orpoorly. This adoption of favorable opinion when a bandwagon starts hasbeen modeled mathematically by Brams and Riker (1972) and Straffin(1977). Their proposal that people preferentially join ascendant groupscould be adopted to ideodynamics by assigning higher infon generationpowers to subpopulations for which the bandwagon had started to roll.

Obviously, these examples are but a few of the wide variety of models inwhich message generation is dependent on prior opinion. Further work istherefore planned to explore these and other models.

Although calculations of messages from opinion have the advantage thatthey permit assessments of social response trajectories without the needto measure real messages, there is the corresponding weakness that thereare no inclusions of the event driven messages which do in fact occur ina significant number of instances. Besides the examples just mentionedfor Lebanon and defense spending, it might be interesting to considerthe example of the habit of smoking. Here, models not permitting theintroduction of unexpected infons would not have been able to accountfor the Surgeon General's report in the early 1960's on the healthhazards of smoking.

Therefore, in further studies, it may be found that satisfactory opiniontime trends can be projected by adding infons calculated from opinion toa minimum number of measured messages from unusual events.

In brief, this book marks a halfway point in a mathematical examinationof persuasion, showing that this process can indeed be analyzed in thetwo separate steps of message generation and message impact. The factthat models of message generation have not yet been fully explored doesnot reflect a weakness of ideodynamics since, as noted above for thelogistic equation, ideodynamics does provide a convenient framework forincluding models for information generation.

To return to the analogy with ballistic missiles, the purpose of thisbook was to examine the effect of messages once launched. To the extentthat an understanding of war requires an understanding of thedevastation due to weapons, an understanding of message impact also isuseful for analyzing persuasion. A satisfactory method for analyzing theeffects of message and weapons provides a solid foundation forconsidering message and missile launching, other key steps in processesof war and persuasion.

This book has demonstrated a consistent predictability of the public inthe face of persuasive information. This finding has certain policyimplications for democratic societies in which the main messages arefrom a trusted press. Since public opinion is heavily influenced by thepress, opinion is not likely to be a direct check on the powers ofindividual groups of elites--like the government, or portions of thegovernment. Instead, the main mechanism may be a number of elitessuccessfully transmitting different sides of a story, thereby checkingthe powers of other elites. For its part, public pinion may just reflectthe messages sent, whatever they may be.

SECTION A Mathematics of Ideodynamics

The essence of the mathematics of ideodynamics has already beenpresented previously (Fan, 1984, 1985a,b). However, new insights weredrawn from the studies in this book so an updated version of the modelis presented below.

In ideodynamics, social changes for a single issue are characterized bysenders transmitting persuasive messages which have impacts onreceivers. Therefore, key elements in the analysis are: the issue withinwhich change is occurring, the messages transmitted, and the structureof the population.

A.1 Structure of Ideas

The first structure to consider is the issue and its associatedpositions, also called ideas. Each issue--essentially a question--isdenoted by Q_(a) where Q refers to all possible issues and index adescribes a particular issue. In this book, six issues were studied soindex a can range from 1 to 6 for the issues. For example, a=1 mightrefer to the issue of whether there should be more, same or less defensespending. Within any issue Q_(a), the public can hold one of severalpositions or ideas indexed by letter j. The individual ideas are denotedby Q_(aj) with a referring to the issue and j to the idea within thatissue. For defense spending, j=1,2,3 could correspond with the positionsof more, same and less spending.

A.2 Structure of the Population

In the general case, P_(a) refers to the population which is likely toreceive information relevant to issue Q_(a). For defense spending, itwas assumed that the Not Sures and Don't Knows were a subpopulationwhich did not receive such information and hence stayed oblivious of thetopic. In this case, P_(a) referred to the remaining population actuallyholding opinions--approximately 90%. Ideodynamics, as described in thisbook, assumes that the population is constant in size and compositionduring the entire time period of any study. At any time t, P_(a) isdivided into two subpopulations, P_(aA) (t) made up of people aware ofthe issue and P_(aU) (t) comprised of persons unaware of the issue butable to receive pertinent information. The fraction of awares P_(aA) (t)within P_(a) is defined as A_(a) (t) so the unawares P_(aU), making upthe balance, constitute (1-A_(a) (t)) of the total population P_(a).Population constancy permits fractions of the public to be usedinterchangably with numbers of people.

Among the awares P_(aA), it is convenient to define the populationfavoring or believing in position Q_(aj) as P_(aAj). The proportion ofbelievers P_(aAj) among the awares P_(aA) is B_(aAj) (t) so the fractionof P_(aAj) within the total population P_(a) is A_(a) (t).B_(aAj) (t).

With this structure, the number of subpopulations is one more than thenumber of positions held by the awares with the unawares comprising theextra subgroup.

A.3 Structure of Messages

Each persuasive message is denoted M_(k) where index k is an arbitrarybut unique number referring to a particular message. Each message M_(k)has three important properties carrying the same index k: a validitycharacteristic of the medium, an audience size, and a number ofcomponents called infons favoring different positions:

1. Validity v_(k) =reputation score for the medium used for transmittingmessage M_(k). In the present studies, all AP messages were all assignedthe constant validity of k_(vAP) characteristic of the AP medium so that

    v.sub.k =k.sub.vAP.                                        (A.1)

Fortunately, this simple assumption gave good opinion calculations forall five examples.

2. Audience size a_(k) (t)=mathematical function of time t describingthe audience size for message M_(k) as time proceeds.

Since AP messages, among many others, are characterized by a highaudience size when the message first arrives at the population, it isconvenient to define two subsidiary message properties:

a. Time t_(k) =the time at which message M_(k) first arrives at thepopulation. For AP messages, it was assumed that t_(k) was the date ofthe AP story. Although there are several hours delay before an APmessage actually appears in the press, the date of the dispatch isprobably not much different from the time of broadcast of television andradio news carrying equivalent information.

b. Initial audience size a_(k) (t_(k))=the number of people exposed toinfon message M_(k) at time t_(k) when the message first reaches thepopulation. This audience size is obviously larger for mass mediamessages than one-on-one conversations. For this book, the initialaudience size was assumed to be the same for all AP messages relevant toan issue. This initial audience size was designated as constant k_(aAP)so, for AP messages,

    a.sub.k (t.sub.k)=k.sub.aAP.                               (A.2)

In contrast to AP infons, the audience sizes for some messages mightactually increase after t_(k). This might happen with a book, forexample, where sales would increase before an eventual decrease.

For mass media messages such as those in the AP, the audience size islikely to decrease rapidly at the same rate for each message after theinitial date. Therefore, a_(k) (t) could be described by an exponentialdecrease with persistence time constant p so that

    a.sub.k (t)=a.sub.k (t.sub.k).e.sup.-p(t-t.sbsp.k.sup.) for t>=t.sub.k =0 for t<t.sub.k.                                            (A.3)

If mass media infons were all retransmitted by opinion leaders or otherpersons with approximately the same kinetics each time, then theanalysis can be performed by incorporating the effect of the opinionleaders into the audience size function for the mass media infons(Chapter 2). The effect would be to prolong the audience size functiona_(k) (t). If a significant amount of time was required for the opinionleaders to begin the retransmission, then the audience size functioncould begin with a short lag after initial receipt of the message by theopinion leader and then be followed by an increase as the opinionleaders began to convey the new information before a final decline asthe opinion leaders stop rebroadcasting the information. Therefore,there would be no need to include opinion leaders explicitly in themodel if their effects can be absorbed into the audience size function.

3. Infons I_(aijk) =components of message M_(k) from different sourcesand directness, indexed by i, and favoring different positions Q_(aj).The indices are discussed in greater detail below:

a. Indices a and j refer to position Q_(aj) within issue Q_(a) and arethe same in I_(aiji) and Q_(aj). Index j corresponds to the first infondimension in Chapter 1.

b. Index i refers both to the directness of the message and to thesource of an idea as deduced from the message itself. Odd indicesi=1,3,5 . . . are used when the source indexed by i directly advocatesposition Q_(aj) while even indices i=2,4,6 . . . are employed whensupport for Q_(aj) is inferred due to information from the sourceindexed by i. The specification of odd and even indices corresponds tothe second infon dimension (Chapter 1). Different index numbers are usedwhen the population can and does distinguish among sources. Theseindividual index numbers, aside from their oddness and evenness indexthe third infon dimension of Chapter 1. Index i can code for bothdimensions 2 and 3 without any ambiguity since dimension 2 only has twopossibilities and these can be represented by oddness and evenness. Theresult is a less cumbersome terminology. The same odd index can be usedfor direct support of Q_(aj) when the population cannot or does notdistinguish among sources. Similarly, the same even index is used forinformation indirectly favoring Q_(aj) when the population does not takethe source into account.

In this book, almost all infon scores were based on phrases explicitlyespousing individual positions and were therefore of the direct variety(odd values of i). Also, the scoring typically did not depend on source.Thus statements by the President of the United States were given thesame weight as quotes from the man on the street. The result for thesecases was all infons being direct and having the same i=1.

The only exceptions were the waste and fraud infons indirectly favoringless defense spending, and the truck bombing infon supporting moretroops for troops in Lebanon. This infon carried the even index i=2since the population had to interpret the news to support more troops.Both the truck bombing and waste and fraud infons were in addition toother infons directly supporting these infons' positions.

Although the positions which infons can favor often coincide with thepolled positions, as happened for the defense spending example, thisneed not necessarily be the case. For example, for the Democraticprimary (Table B.3, Section B), the polled positions were: Pro-Mondale,Pro-Glenn, Pro-Others and No Opinion. However, the infons in thebandwagon analysis (Chapter 4) supported the six positions of:Pro-Mondale, Pro-Glenn, Pro-Others, Con- Mondale, Con-Glenn, andCon-Others.

Therefore, index j refers to all ideas Q_(aj) under considerationincluding both those favored by corresponding subpopulations P_(aj)and/or those supported by corresponding infons I_(aijk). Polls mightshow no persons favoring some ideas Q_(aj). For instance, there were nopoll measurements for persons opposed to Mondale (Con-Mondale).Similarly, there were no infons associated with some polled positions.For example, no infons were scored as favoring No Opinion in theDemocratic primary study. However, there will usually be a substantialnumber of positions which both people and infons will favor. To continuewith the Democratic primary, Pro-Mondale was both a polled position anda position which some infons supported.

c. Index k=the same index k as that in message M_(k) containing infonI_(aijk). Thus k indexes the fourth dimension of Chapter 1.

In summary, infon I_(aijk) refers to the infon from persuasive messageM_(k), with source and directness i, and supporting position Q_(aj) ofissue Q_(a). With this structure, a persuasive message is analyzed as acollection of infons all acting on the population. There can be a numberof different infons from different sources indexed by i all supportingthe same position Q_(aj).

A.4 Nomenclature Simplification

For the rest of this section, the nomenclature will be simplifiedcorresponding to only one issue studied at a time as was true for eachcase in this book. The restriction to a single issue means that index areferring to the issue is always the same in any one analysis and can bedropped from all terms during calculations for that issue. Also,believers P_(aAj) in position Q_(aj) were necessarily aware of thatposition, so index A specifying awareness is redundant and is thereforedropped from P_(aAj) and their associated percentages B_(aAj). With theomissions of subscripts a and A, the subpopulations and theirpercentages are simply denoted as P_(j) and B_(j).

After removal of subscript a, the remaining subscripts for infons are ifor infon source, j for infon position and k for the message carryingthe infon.

A.5 Infon Properties

Since ideodynamics treats infons I_(ijk) as components of message M_(k),each infon also has a validity characteristic of the medium, an audiencesize and a content score:

1. Medium validity v_(ijk) of infon I_(ijk) =v_(k) for the parentmessage M_(k). This equality holds regardless of the source indexed by ior the position favored Q_(j) because the medium is the same for allinfons of a message.

2. Audience size a_(ijk) (t) of infon I_(ijk) =a_(k) (t) regardless of iand j since all corresponding infons are portions of the same messageM_(k).

3. Content c_(ijk) of infon I_(ijk) =content score for the infon. Thisscore gives the extent to which the infon favors position Q_(j). In thisbook, AP infons were given content scores in terms of typical APparagraphs. This provides a method for comparing the content strengthsof different infons.

A.6 Infon. Persuasive Force

These infon properties are used to calculate functions f_(ijk)describing the "immediate persuasive force" of infon I_(ijk) at time t:

    f.sub.ijk (t)=c.sub.ijk.v.sub.k.a.sub.k (t)                (A.4)

This function states that the population's exposure to an infon'spersuasive power at time t is proportional to the infon's content andvalidity scores and to the audience size.

The exposure of the population to all persuasive infons I_(ijk) is givenby a "combined immediate persuasive force" function F_(ij) assumed to bethe sum of the persuasive force functions f_(ijk) (t) for all infonsreceived before time t. Therefore,

    F.sub.ij (t)='.sub.k f.sub.ijk (t)                         (A.5)

for all k with t_(k) <t. This summation of individual functions withexponential time dependent decays was also used by Hibbs (1979, see hisEquation 2) to explore the issue of unemployment versus inflation, atopic also studied in this book.

A.7 Information Influencing the Unawares

Only infons directly supporting idea Q_(j) (odd index numbers i) shouldbe able to raise the consciousness of those unaware of the issue. Incontrast, indirect infons (even index numbers i) do not make a directstatement about the issue and will be ignored by the unawares. Sinceonly F_(ij) with odd numbers i act on the unawares, it is convenient todefine function F_(j) summarizing the total information in favor of ideaQ_(j) available to the unawares where

    F.sub.j (t)='.sub.i F.sub.ij (t)                           (A.6)

for all odd i.

A.8 Information Influencing the Awares

The unawares should not be able to remember information which did notraise their consciousness. Those unawares who are able to remember wouldalready have become aware. In contrast, the awares should be able toremember. Therefore, information will act on the awares and unawareswith different time courses.

To model this effect of memory, consider an infon I_(ijk) arriving atthe population at time t_(k). To compute the effect of this infon at alater time t, it is convenient to divide the time between t_(k) and tinto very small intervals. Consider one such interval between time t'and (t'+dt'). The chances that information will have been received bythe awares at this time will be f_(ijk) (t'). If this information islost from the consciousness of the the awares in an exponential fashione^(-m)(t-t') with memory constant m, then the persuasive force due toinformation remaining at time t will be the chances that the informationwas received at time t' times the probability that the information hadnot been forgotten, in other words f_(ijk).e^(-m)(t-t'). However, thenumber of awares may have been different at different times t' in thetime interval from t_(k) to time t. In this case, the more the awares ata given time t', the more would have been the amount of informationreceived in that time interval t' to (t'+dt') and the greater would havebeen the persuasive force at time t. Therefore, the persuasive effect ofinformation from that time interval is also proportional to the fractionof awares A(t') at time t' (see Section A.2 above). As a result, theresidual persuasive influence at time t of information absorbed at timet' is A(t').f_(ijk).e^(-m)(t-t'). The total effect of all information attime t from infon I_(ijk) is then the integral from the time of infonreceipt by the population to the measurement time: ##EQU10## whereg_(ijk) (t) is the "remembered persuasive force" function describing theremaining information from infon I_(ijk) available for action on theawares at time t.

In all the polls in this book, the percent of Not Sures, No Opinions,and Don't Knows were all typically less than 10% so that awareness couldbe considered to be essentially 100% in which case A(t')-˜1. Thisassignment followed by substitution of Equation A.2 in A.3, insertion ofthat result together with Equation A.1 in Equation A.4 and then furthersubstitution in Equation A.6 yields ##EQU11## Explicit evaluation ofthis integral yields ##EQU12##

This function g_(ijk) (t) will approach a single exponential decay wheneither (m>>p) or (p>>m). In the case of much larger m, the secondexponential in Equation A.9 would quickly become negligible and wouldeffectively mean that an infon is able to influence the population onlya very short time after it is received. In other words, there would bevery little memory of an infon for the purposes of opinion change.Should p be much greater than m, the first exponential in Equation A.9would be negligible shortly after t_(k). This would be equivalent to theinfon appearing and disappearing almost instantaneously with most of aninfons' effect begin due to continued persuasion from remembered infons.As m and p approach each other, the g_(ijk) function has significantvalues at progressively longer times.

Function G_(ij) (t) can be constructed to describe the "total rememberedpersuasive force" acting on the awares due to information from allprevious infons I_(ijk). As for Equation A.5, this function is the sumof the persuasive forces from the individual infons so that

    G.sub.ij (t)='.sub.k g.sub.ijk (t)                         (A.10)

for all k with t_(k) <t.

Since the awares, unlike the unawares, are susceptible to infons withboth odd and even indices i, the total information G_(j) in favor ofposition Q_(j) will be the sum of the G functions over all indices i.That is,

    G.sub.j (t)='.sub.i G.sub.ij (t)                           (A.11)

for all i. Again, this equation has a form similar to Hibbs' (1979)Equation 2.

As noted in Chapter 1, Equation A.10, although successful for this book,ignored the hardening of the viewpoints of the various subpopulationsdue to reinforcing infons. In the concept of reinforcement, asubpopulation P_(r) supporting position Q_(r) will be reinforced byinfons I_(irk) favoring the same position Q_(r). Therefore, thepersuasive force of infons I_(ijk) pulling subpopulation members awayfrom position Q_(r) and toward position Q_(j) will no longer be G_(j)but will be diminished. "Diminished persuasive force" functions H_(jr)(t) can be constructed to account for this decreased effect. Thesefunctions H_(jr) include the persuasive force of infons supportingposition Q_(j) being diminished by reinforcing infons favoring positionQ_(r).

One function which would have the right properties would be

    H.sub.jr (t)=G.sub.j (t)/(d.sub.jr.G.sub.r (t)+1)          (A.12)

where d_(jr) is a diminution constant describing the decrease in thepersuasive forces of infons I_(ijk) due to the reinforcing effects ofinfons I_(irk). If d_(jr) is very small small, there is no reinforcementand H_(jr) =G_(j). As d_(jr) increases, the reinforcing powers of infonsI_(irk) also grow. According to Equation A.12, the greater thereinforcing power G_(r), the lower would be the conversion force due toG_(j).

Corrected persuasive force functions H_(jr) can be further modified totake into account people becoming saturated due to continued andfrequent repetitions of the same infons favoring position Q_(j) :

    H.sub.jr (t)=G.sub.j (t)/(d.sub.jr.G.sub.r (t)+d.sub.jj.G.sub.j (t)+1). (A.13)

As for d_(jr), d_(jj) is a constant for the saturation effect due tooverrepetiton of infons I_(ijk). The addition of the d_(jj).G_(j) termin the denominator again means that there is little saturation due tomore infons I_(ijk) if d_(jj) is small. On the other hand, it is easy tosaturate if d_(jj) is large.

A.9 Effect of Information on the Population

Ideodynamics permits any number of positions Q_(j). For example, oneposition may be aware but uncommitted. Some of the Don't Knows measuredin opinion polls may have been of this type while others may have beenunaware. In all the studies in this book except that for the Democraticprimary, it was assumed that all the Don't Knows were unaware and stayedunaware throughout the polling period. For the bandwagon analysis forthe Democratic primary, it was assumed that the No Opinions wereactually aware but uncommitted. The actual situation may have beensomewhere in between. However, the total percentages of the populationwere typically less than 10% so the calculations are not stronglydependent on assumptions for the Don't Knows.

A general differential equation can be written to describe the timedependent changes in the number of people A(t).B_(j) (t) belonging tosubpopulation P_(j) : ##EQU13##

In the terms on the right, the first double sum over j' and r gives thegain in subgroup P_(j) due to recruitment from other subpopulationsP_(r), while the second double sum over j' and r gives the loss ofmembers from P_(j) due to information favoring other positions. Thesingle sum over j' gives the gain in subgroup P_(j) due to recruitmentfrom the unawares, and the last term reflects loss from P_(j) tounawareness due to forgetting. The detailed explanation of these termsis as follows:

Recruitment from other subpopulations --first double sum in EquationA.14. Members of a particular subpopulations P_(r) can be persuaded bypersuasive force functions H_(j'r) to join subgroup P_(j). Resistance tochange due to reinforcing infons I_(irk) and saturation with infonsI_(ij'k) has already been incorporated into H_(j'r) (Equation A.13). Thenumber persuaded at time t is proportional to the pool of potentialconverts P_(r). The size of this pool from Section A.2 is A(t).B_(r)(t). The number converted is also proportional to the persuasive forcefunction H_(j'r) describing the effectiveness of infons I_(ij'k) in theface of reinforcing infons I_(irk). The constant of proportionality isk_(2j'r), the "persuasibility" constant with subscript 2 indicating thatthe constant is a persuasibility constant.

The summation is over all possible j' and r with values of k_(2j'rj)=constant if persuasive force function H_(j'r) can actually persuademembers of subpopulation P_(r) to change their opinions away fromposition Q_(r) and join subpopulation P_(j) favoring idea Q_(j). Thek_(2j'rj) all have the same positive value denoted by k₂ for allpermitted conversions for any one issue. This is consistent with Chapter1 postulating that the persuasibility constant measures the closeness ofthe issue to the core beliefs of the population. However, the k₂ canchange from issue to issue.

Constant k_(2j'rj) can be postulated to be positive for any combinationof indices. For instance, k_(2j'rj) =k₂ is possible for two different j'but the same r and J. This would mean that two types of persuasiveforces can persuade members of the same target population P_(r) to jointhe destination population P_(j). An example would be informationfavoring both more and same defense spending persuading people favoringless spending to support same spending. In contrast, k_(2j'rj) =0 if atransition is not permitted. For example, a persuasive force functionfavoring less defense spending should usually not persuade peoplefavoring less spending to favor more spending. The entries in the arrayof all k_(2j'rj) therefore has entries of either k₂ or 0. The details ofthe array correspond to the postulated "population conversion models forthe awares" (see FIGS. 5.2, 5.18, 5.29, 5.30, 5.35, 5.39, and 5.45 ofChapter 5 for examples).

Loss of believers--second double sum in Equation A.14. This sum merelyreflects the fact that the population stays constant in size. If peopleare persuaded by persuasive force H_(j'j) to leave population P_(j) tojoin population P_(r) with k_(2j'jr) =k₂, then there will not only be again in population P_(r) but also a loss from population P_(j). Thisloss results in the second sum in Equation A.14 having a negative sign.The magnitude of the loss is the same as the gain by population P_(r) sothe terms in the first two sums of the equation have the same form.

Conversions of unawares to awareness--single sum in Equation A.14. Theseconversions are based on the argument that the unawares cannot rememberinformation so they learn about the issue through infon persuasive forcefunctions F_(j') (t) (Equation A.6). The rate at which the unawaresP_(U) move to hold position Q_(j) due to F_(j') (t) will be proportionalboth to the number of people (1-A(t)) in P_(U) and to F_(j') with an"attentiveness" constant of proportionality k_(1j'j). Subscript 1denotes an artentireness constant (Chapter 1). Constant k_(ij'j) has thesame structure as constant k_(2j'rj). However, since the only targetpopulation under consideration is the unawares P_(U) there is no need tospecify the target population in the constant. It is sufficient tospecify the index j' for the persuasive force F_(j') and index j for thedestination population P_(j). As with k_(2j'rj), k_(1j'j) either has aconstant value denoted by k₁ or a value of zero depending on a chosen"population conversion model for the unawares." The sum in Equation A.14is over all j'. If there is no contribution from a function F_(j'), theconsequence would be k_(1j'j) =0 for the corresponding attentivenessconstant. For instance, if the model proposes that all functions F_(j')first move the unawares into a population P_(j) of aware butuncommitted, k_(1j'j) =k₁ only for this one value of j. All otherk_(1j'j) =0.

Forgetting of a position--last term of Equation A.14. In the reverseprocess, it is assumed that any aware can forget the issue and becomeunaware. The unlearning of the issue is characterized by constant u withthe rate of conversion of awares favoring position Q_(j) to unawarenessbeing u.A(t).B_(j) (t). In this expression, the chances of forgettingare the same for all individuals so the total loss from awares favoringidea Q_(j) is proportional to the size of the correspondingsubpopulation A(t).B_(j) (t).

As argued for Equation A.7, the Don't Knows in this book were usuallyless than 10% so that A(t)-˜1. Then Equation A.14 becomes ##EQU14## withnegligible recruitment of persons from the unawares and essentially noforgetting of the issue.

If @t is a small time interval, then integration of equation A.15 fromt-@t to t yields ##EQU15## If the H and B functions do not changesubstantially during the time interval t-@t to t then Equation A.16 canbe approximated by

    B.sub.j (t)=B.sub.j (t-@t)+(.sub.j',r '[k.sub.2j'rj.H.sub.j'r (t).B.sub.r (t-@t)-k.sub.2j'jr.H.sub.j'j (t).B.sub.j (t-@t)]).@t      (A.17)

This deterministic equation has no stochastic terms because it will beshown (Chapter 5, FIGS. 5.10 and 5.23) that calculations of B_(j)several time intervals @t after the beginning of the computation arerelatively independent of initial values for the various B_(j).

A.10 Modifications for AP Infons Assuming No Unawares

For all examples in this book it was assumed that the AP wasrepresentative of mass media messages without pretending that this wireservice included all mass media communications. Furthermore, theanalysis used only random samples rather than all AP dispatchesidentified as appropriate to the issue being studied. Therefore, theequations needed to be modified to account for all the facts that onlysome of the relevant dispatches were examined and that some relevantmessages may not have been identified by the methods used to find thepertinent communications.

This book (see Chapter 5) demonstrates that functions g_(ijk) for all APinfons gave good projections for all six examples using an exponentialdecay with a half-life of approximately one day. With such a shorthalf-life, it is likely that the persistence constant p is the dominantterm with forgetting begin extremely rapid such that m>>p so that thesecond exponential in Equation A.8 can be neglected giving

    g.sub.ijk (t)=k.sub.AP.c.sub.ijk.e.sup.-p(t-t.sbsp.k.sup.) (A. 18)

where

    k.sub.AP =k.sub.vAP.k.sub.aAP /(m-p).                      (A.19)

During the total time for which opinion calculations were made, letT=total number of AP dispatches identified as potentially relevant,T_(I) =number of irrelevant AP dispatches identified erroneously usingthe search command passed to the Nexis data base, T_(M) =number ofpertinent AP dispatches missed during the identification process,R=number of dispatches actually retrieved, R_(I) =number of irrelevantdispatches among the number retrieved, R_(M) =number of pertinent butmissed dispatches, S=R-R_(I) =number of dispatches among the retrievedwhich were shown to be relevant during the final scoring process, andG'_(j) =persuasive force function calculated from the random subset ofall relevant infons. Recall that G_(j) is defined as the persuasiveforce function if all relevant infons and not a random subset is used.

If it can be assumed that dispatches in the T_(M) group which were notidentified as relevant had the same infon persuasive forces as those inthe identified group T, then

    G'.sub.j =[S/(T-T.sub.I +T.sub.M)].G.sub.j                 (A. 20)

since the G' is a fraction of G corresponding to the truly relevantnumber of dispatches S divided by the number of dispatches from whichthe sample should have been drawn, namely the relevant dispatchesactually identified (T-T_(I)) together with the ones which were missedduring the search (T_(M)).

If the retrieval is random, then the fraction of irrelevant dispatchesshould be the same among the retrieved stories and the identifieddispatches from which the random set was drawn so that

    R.sub.I /R=T.sub.I /T.                                     (A.21)

From Equations A.20, A.21 and the relationship S=R-R_(I) (seedefinitions), the result is

    G.sub.j =(T/R).([S+(R/T).T.sub.M ]/S).G'.sub.j.            (A.22)

In other words, the G function needed for the ideodynamic equations canbe replaced by the G' functions calculated from a random subset of theretrieved messages if the G' is multiplied by (T/R) the ratio of thetotal dispatches to those retrieved for the analysis whenever T_(M) =0meaning that the procedures used for the message identification did notmiss any important messages. Thus the assumption of no missing messagesmeans that Equation A.22 becomes

    G.sub.j =(T/R).G'.sub.j.                                   (A.23)

Besides sampling mass media infons from AP dispatches alone and thenretrieving only a random set of stories identified as relevant, it wasfurther assumed that d_(jr) =d_(jj) =0 for all H_(jr) so that H_(jr)=G_(j) from Equation A.13. This assumption was used because it gave goodcalculations for all the cases in this book (see Chapter 5). Therefore,in applications to a random sample of relevant messages,

    H.sub.jr =(T/R).G'.sub.j.                                  (A.24)

It is further convenient to define a "modified persuasibility constant"k'₂ of the form

    k'.sub.2 =k.sub.2.k.sub.AP =k.sub.2.k.sub.vAP.k.sub.aAP /(m-p) (A.25)

using Equation A.19.

Substitution of Equations A.9, A.18, A.24, and A.25 in Equation A.17gives ##EQU16## for all j' and r where G"_(j) are "skeleton persuasiveforce" functions of form ##EQU17## The summation is over all i and for kwith t_(k) <t. For consistency with Equation A.26, Equation A.27 usesindex j' instead of index j employed in earlier persuasive forcefunctions.

Empirically measured scores for c_(ij'k) are denoted s_(ij"k). Scoress_(ij"k) carry the same index i for the source of the information and kfor the message for which the score was obtained. However, index j" forthe position which a score favors might not always coincide with indexj' for the position of the infon I_(ij'k). The reason is the difficultyin assigning some scores to a particular position. For instance, anexhortation to cut an increase in defense spending might be interpretedby some people as still favoring more defense spending, although less ofan increase, while others might feel that defense spending should not beincreased but held the same. Therefore, this exhortation might be scoredas favoring same spending but might actually support both more and samespending. If the average score favoring same defense spending reflects asignificant component favoring more spending, then there should be amechanism to permit the score favoring same spending to contribute topersuasive forces for both same and more spending.

This is done by introducing "refining weight" constants w_(ij'j"). Theseweights specify the contribution of a score s_(ij'k) to content scoresfor infons I_(ij"k) favoring positions Q_(j"). Weights w_(ij'j") permita measured score to contribute to the content score of more than oneinfon using the following equation: ##EQU18## The summation here is overall positions indexed by j" which the scores can favor. In fact, some ofthese positions may not even coincide with the positions Q_(j') of theinfons used in the analysis. For example, Chapter 5 considers scoresfavoring waste and fraud by defense contractors to contribute to infonsfavoring less defense spending.

In the most straightforward case, however, every score measured to favorposition Q_(j') would also contribute only to the content score of aninfon supporting the same position Q_(j'). In this case, w_(ij'j") wouldonly be positive when j'=j", and positions of infons indexed by j' wouldcoincide with the positions of the scores indexed by j".

Substitution of Equation A.28 in Equation A.27 yields ##EQU19## for alli and j" and for all k with t_(k) <t.

For AP stories, opinion Calculations can be made using Equations A.26and A.29. Functions G"_(j') (t) can be calculated at any desired timefrom a table of w_(ij'j") formulated by the analyst, from t_(k) for thecollected AP stories, and human or machine coded s_(ij"k) scores. Inthis book, the computer assisted computations of s_(ij"k) scores aredescribed in Chapter 4. Calculated values for functions G"_(j') (t) areinserted into Equation A.26 for computations of opinion time trends. Thek'_(2j'rj) values and constant p are specified by the analyst. T/R isthe reciprocal of the fraction of the AP dispatches retrieved fordetailed analysis among all identified as relevant to the issue. Thecomputations can begin with an initial set of B_(j) (t-@t) correspondingto the first available public opinion poll. Calculations for publicopinion are then made by increasing time t by intervals of @t specifiedby the analyst. The values of B_(j) (t-@t) for these subsequentcalculations are the B.sub. j (t) values from the calculation one @tearlier.

Examination of Equations A.26 and A.29 shows that all contributions toB_(j) (t) include a k'_(2j'rj) or k'_(2j'jr) term multiplied by aw_(ij'j") term. Therefore, the modified persuasibility constant isalways multiplied by a refining weight. Since the refining weight canhave any value, it is possible that the modified persuasibilityconstants actually did not have the same value as postulated forEquations A.14 and A.26. It may be possible, as was observed for troopsin Lebanon and aid to the Contra rebels (see Chapter 5), that the publicmay be more difficult to convince for some positions of an issue. Inthis case, the k'_(2j'rj) in the same analysis could be different fordifferent positions with the w_(ij'j") incorporating those differencesin addition to those due to scoring ambiguities. However, in all theanalyses of this book, all refining weights were quite similar with thelargest ratio between the most disparate weights being less than 3.0. Onthe other hand, the modified persuasibility constants had 50-folddifferences. Therefore, it is indeed convenient to think of an issue asbeing characterized by a global persuasibility constant which can varywidely from issue to issue (see Chapter 1). The refining weights thenreflect the remaining minor differences in the persuasibility constantand/or the effectiveness of statements from different sources supportingdifferent positions.

Since the persuasibility constant k₂ --and hence the modifiedpersuasibility constant k'₂ --can vary from issue to issue (see EquationA.26), it was necessary find the optimal value for this constant. It wasalso important to optimize the persistence constant and the refiningweights w_(ij'j"). All optimizations were performed for each polledtopic by minimizing the differences between the calculated B_(j) (t) andthe B_(j) (t) from published polls.

The method was a least squares optimization where a number of possiblevalues for the variable parameters were chosen by the analyst andcomputer simulations were made using Equation A.26. For each set ofvalues, a public opinion time trend was computed over the entire timeinterval of the polls, deviations were measured between the actual pollpoints and the calculated opinion curves for all points of view. Therewere as many opinion projection curves as positions polled. Thedeviations between measured and projected poll percentages were computedfor all curves, squared and averaged to give the mean squared deviation(MSD). Optimizations were performed by plotting various trial constantsagainst this MSD to find the value with the minimum MSD. These plotsshow the sensitivity of the fit to values of the optimized constants. Ifthe MSD plots are relatively flat for increasing values of theparameter, then the calculations are relatively insensitive to changesin the constant in the region of the minimum MSD.

Sometimes, the square root was taken of the MSD to give the root meansquared deviation (RMSD). This RMSD can be compared directly with thestandard error calculated by taking the square root of the pollpercentage times 100 minus the poll percentage divided by the samplesize. If the poll points deviate randomly from the calculated opinion,the differences between the calculated opinion and calculated pollvalues should follow a normal distribution with the standard deviationbeing equal to the RMSD.

Unfortunately, it is not permissible to perform the usual r²regressions--without making assumptions which are difficult to justifyrigorously--between opinion poll values and opinions calculated fromEquations A.26 and A.29. This is because opinions at earlier times aredependent on opinions at later times. Since the independence conditionsneeded for the regressions are not met, the more descriptive MSD andRMSD were used for statistical comparisons. These calculations alsoprovided a convenient set of numbers for optimizing the parameters ofthe model.

A.11 Comparison with Uniform Distribution

One of the most fundamental questions is whether the poll estimates areany better than those obtained by picking poll points at random. Thestraightforward test in this book used Monte Carlo simulations.

At each poll time, a set of random poll points all adding to 100% wascalculated from a set if random numbers. Then squared deviations werecalculated between each of the measured poll points and itscorresponding value drawn at random. These squared deviations were thenaveraged to give the precise equivalent to the MSD computed usingoptimal constants in the ideodynamic equations. Therefore every MonteCarlo simulation based on random numbers also yielded the exact analogof the MSD calculated from AP dispatches.

A thousand of these simulations were performed for each set of opinionprojections yielding 1000 MSD's based on randomly calculated pollpoints. The fraction of these MSD's which were less than the MSD'scalculated by ideodynamics gave the probability that the ideodynamicestimates could be obtained by chance alone.

A.12 Modification for AP Infons Assuming Non-negligible Unawares

In Section A.10 above, the assumption was made that there wereessentially no unawares. If this group cannot be neglected, then opinioncalculations are made using Equation A.14. If both sides of EquationA.14 are summed over all j the result is ##EQU20## since the sum of overall B_(j) (t)=1 by the definition of B_(j) (t). Also, the terms in thetwo double sums of Equation A.14 cancel each after summation over j.Like the approximation of the solution of Equation A.15 by EquationA.17, the solution of Equation A.30 can be approximated by ##EQU21## Solong as functions F_(j') (t) can be computed, this equation can besolved if constant u, an initial value for A(t), and all k_(1j'j) arealso provided. From Equations A.1, A.2, A.3, A.4, A.6, and A.28,##EQU22## for all k with t<t_(k) and for all odd i. Since k_(1j'j) =k₁or 0 (see discussion following Equation A.14), and since k_(1j'j) inEquation A.31 is always multiplied by k_(vAP) and k_(aAP), it isconvenient to define a "modified artentireness" constant

    k'.sub.1 =k.sub.1.k.sub.vAP.k.sub.aAP.                     (A.33)

Then, Equations A.31 and A.33, together with analogs to EquationsA.20-A.23, yield ##EQU23## for all j and j' where ##EQU24## for all oddi, all j", and k with t<t_(k).

With Equations A.34 and A.35 and postulated values of k'_(1j'j) --inaddition to the values of Section A.10 above, it is possible tocalculate A(t) at intervals of @t if an initial value of A(t),corresponding to those who had not yet heard of the issue, is availablefrom a poll. Then, with the postulated values of k'_(1j'j) andcalculated A(t), it is possible to calculate the fraction of the totalpopulation favoring position Q_(j) by approximating the solution ofEquation A.14 by ##EQU25## A.13 Extensions to Very Long Times

As noted in Section A.2 above, the assumption was that the populationwas constant during the time period of the calculation. In other words,birth death, and migration into and out of the population were ignored.These assumptions are likely to be valid for the periods under a yearused for the Lebanon and Democratic primary examples (see Chapter 5).However, as times increased to over 9 years for the case of defensespending, this assumption may start to break down. To account for death,it is possible to introduce another term like that at the end ofEquation A.14. To account for birth, it is necessary to add additionalterms to Equation A.14 adding members to the population of unawares.Then their conversion to awareness can follow the discussion in SectionA.12 above. Furthermore, in the absence of population constancy, all theequations of this chapter will also need to be modified so that allcomputations are in terms of absolute number and not percentages.

There is another place where the model may need to be changed. Theconstants in Equation 14 and its derivative equations may slowly changewith time as society changes. For instance, the reputation of the mediummay slowly drift. Therefore, for truly long term studies, the constantsmay have to be converted into time dependent functions reflecting theaccumulated experience of the population.

A.14 Models with No Dependence on Subpopulations

Unlike ideodynamics, some models do not calculate opinion percentagesbased on a subdivided population. Instead, only forces on the populationas a whole are taken into account. Ideodynamics can also be used tocalculate opinion based solely on persuasive forces by making theassumption that opinion change is sufficiently slow so that dB_(j)/dt-˜0. In this case, Equation A.15 converts to ##EQU26## With dB_(j)/dt-˜0, all the B_(j) are constants and calculable given only thepersuasive forces H_(jr) and the persuasibility constant k_(jr).Equation A.37 is actually a system of simultaneous equations, one foreach position with subscript j. With j equations, there is a uniquesolution for each of the B_(j). Given no rapid change in B_(j), the Hfunctions which drive the change must also be reasonably constant atcalculation time t.

Section B Data for Calculating Opinion Change

The data used for projecting public opinion were of two types: (1) timeseries of public opinion polls from published data, and (2) APdispatches relevant to the polled topics retrieved from the Nexiselectronic data base sold by Mead Data Central, 9393 Springboro Pike,P.O. Box 933, Dayton, Ohio, 45401. This data base contained all APdispatches since Jan. 1, 1977. Polls and AP dispatches were obtained forfive issues.

All retrievals were restricted to text within 50 words both before andafter one of the keywords used in the original search. The 50 word limiteliminated irrelevant sections of the dispatches and was chosen becausethe words at the beginnings and ends of the retrieved regions typicallyshowed transitions to other topics. Articles concentrating on the issuetypically had the key search words within 100 words of each other (50words after one search word and 50 words before another). These articleswere automatically retrieved in entirety.

All AP searches began before the first poll point in order to accountfor the residual effects of prior messages. The search was made for alldispatches up to six months before the first point in the poll seriesunless the six month period extended before the beginning of the database on Jan. 1, 1977. In that case, the search began with this date. Allsearches stopped at the end of the polling period.

B.1 Defense Spending--1977-1984

Four variant Doll series were found from 1977-1984 (Table B.1) for theissue of whether more, same or less should be spent on defense. Althoughearlier polls existed, they were not studied because the Nexis data baseonly contained AP dispatches back to 1977.

In all polls, the vast majority of the population had definite opinionsand were divided into three groups: those favoring more, same or lessdefense spending. There was also a group of Don't Knows or Not Sures,typically in the range of 5-10%. This subpopulation was subtracted fromthe total and the poll data was renormalized among all those with anopinion.

For ideodynamics, this step effectively assumed that the small number ofpersons with no opinion stayed in that category. Even if this assumptionwas not entirely valid, the numbers were sufficiently small that theresults would not have been much affected.

Fortunately, the same time trend was seen for all four polls after theDon't Knows were removed (FIG. 2.1). For this figure, no adjustmentswere made beyond the removal of those with no opinion. Given theagreement between the different poll series, the data were pooled.

Relevant information in the Nexis data base was identified by searchingthe full texts of all dispatches using combinations of key words chosenby the investigator.

For defense spending, the search was for (DEFENSE or MILITARY or ARMS)within 5 words of (BUDGET! or EXPENDITURE or SPEND! or FUND!) from Jan.1, 1977 to Apr. 1, 1984. The ! permitted the trailing characters to beanything so that both budgeted and budgetary would have been found withbudget!.

The search command yielded 9314 dispatches with Nexis numbering thedispatches in reverse chronological order from 1 being the most recentto 9314 being the earliest. From a random 692 of these dispatches, textwas retrieved if it was within 50 words of one of the seven key searchwords given in the previous paragraph. If two key words occured within100 words of each other, the entire intervening text was collected. Thetotal retrieval was 820,000 characters of text.

A sizable number dispatches were not about American defense spending. Assoon as this became clear, the retrievals were stopped.

The analyses using the 692 AP dispatches from 1977-1984 and the polldata in Table B.1 were extended in two ways. First, additional poll datawere collected from the Roper Center at the University of Connecticut(see Table B.6 below for more details) from January 1977 to April 1986.A time series of 62 separate polls could be obtained by pooling theseadditional polls those in Table B.1. Besides polls from the NationalOpinion Research Center, NBC News, and the Roper organization, pooledpolls also contained results from ABC News, CBS News, and the Gallup andHarris and organizations.

The same commands used for identifying the 9413 dispatches from1977-1984 were used again to locate 10,451 stories from Jan. 1, 1981 toApr. 12, 1986. Of these 1067 were retrieved randomly for extending thestudy to 1986.

To determine the importance of stories on waste and fraud on opinion ondefense spending, the Nexis data base was further searched for (DEFENSEor MILITARY or ARMS) within 5 words of (WASTE or FRAUD or CORRUPTION)from Jan. 1, 1977 to Apr. 12, 1986 yielding 878 dispatches of which 512were retrieved at random for text within 50 words of one of the searchwords.

B.2 Troops in Lebanon--1983-1984

A poll single series provided opinion for whether more, same or lesstroops should be sent to Lebanon in 1983-1984 (Table. B.2). As fordefense spending, the No Opinions were in the 5-10% range and weresubtracted from the total. The other opinions were renormalized to 100%and the resulting values were used for the remainder of thecalculations.

Pertinent AP dispatches were again retrieved from the Nexis data base,this asking for (LEBAN! and ((AMERICA! or U.S. or UNITED STATES)preceding by two words of less the words (TROOP or MARINE or FORCE)))from Mar. 26, 1983 to Jan. 17, 1984. The search began six months beforethe first poll point (Mar. 26, 1983) and ended with the last poll date.

The search yielded 1517 dispatches among which 467 were retrieved atrandom for 1,570,000 characters of text. As for defense spending, theretrieval was for text within 50 words of one of the search words.

B.3 Democratic Primary--1983-1984

The polls for candidate preference before the Iowa caucusses were fromABC News (Table B.3) and ran from Jun. 19, 1983 to Feb. 15, 1984. Duringthis time, the major candidates were John Glenn and Walter Mondale.There were other candidates, but none of their percentages ever exceeded15% so those percentages were all pooled.

AP dispatches were retrieved if they contained the name of at least oneof the candidates tallied in the polls. The search was for (RUBINpreceding ASKEW by two words or less) or (ALAN preceding CRANSTON withintwo words or less) or (JOHN preceding GLENN by two words or less) or(GARY preceing HART by two words or less) or (ERNEST preceing HOLLINGSby two words or less) or (JESSE preceding JACKSON by two words or less)or (GEORGE preceing MCGOVERN by two words or less) or (WALTER precedingMONDALE by two words or less) from Dec. 19, 1982 to Feb. 15, 1984. Asfor troops in Lebanon, the search began 6 months prior to the first polldate.

Although the last names would probably been sufficient for relativelyrare names like Cranston and Mondale, it was necessary to include thefirst names due to more common names of which Jackson would have beenthe most ambiguous. Therefore, the search used the condition that thefirst name of the every presidential candidate preceed the last name byno more than two words. With this condition, a middle initial could bepresent in the names some of the time and missing in others.

The search yielded 2435 dispatches of which 425 (1,100,000 characters)were retrieved at random for text within 50 words of a search word.

B.4 Economic Climate--1980-1984

The polls for public opinion on the economic climate were taken by ABCNews (Table B.4) and covered a three year period from March 1981 toJanuary 1984. The no opinion category was very low at all times, neverexceeding 3% so this fraction was subtracted and the other percentageswere renormalized to 100% for the calculations.

AP dispatches in the Nexis data base were searched for (ECONOM! within25 words of (CONDITION! or HEALTH or PROSPECT! or FUTURE or FORECAST! orOUTLOOK! or PROJECT!)) from Sep. 6, 1980 to Jan. 17, 1984. This searchalso began six months before the first poll date.

A total of 12,393 dispatches were identified of which 461 (730,000characters) were retrieved at random for text within 50 words of asearch word.

B.5 Unemployment versis Inflation--1977-1980

Polls from NBC News asked about the relative importance of unemploymentand inflation (Table B.5). The 3% or less of the population who were notsure were subtracted and the remaining percentates were renormalized.

AP dispatches on this topic were identified searching for (UNEMPLOY!within 25 words of INFLATION) from Jan. 1, 1977 to Aug. 23, 1980. Thesearch began with the beginning of the data base in January 1977 aboutthree months before the first poll date.

The search identified 1591 AP dispatches of which most (1582 with2,300,000 characters) were randomly retrieved for text within 50 wordsof a search word.

B.6. Contra Aid--1983-1986

Polls on the topic of whether aid should be sent to the Contras fightingthe government of Nicaragua were obtained from the four organizationslisted in Table B.6. Despite significant wording differences from pollto poll, there was very little change in opinion during the entirepolling period, so all polls were pooled. The criteria for choosing thepolls was that they ask about American opinion on either aid with noqualifiers or on both military and non-military aid. No published pollsfound before the last poll in Table B.6 were excluded if they met thesecriteria and were in the POLL data base at the Roper Center. The CBS-NewYork Times poll was obtained independently from CBS News and was theonly additional poll found meeting the criteria given above.

For all tables, the polling date was assumed to be the midpoint betweenthe beginning and the end of the polling period. Where no date wasgiven, the midpoint of the polling month was used.

Table B.1

Polls on the desirability of increasing defense spending. The data werefrom four different variants of polls on defense spending compiled withthe aid of B. I. Page, R. Y. Shapiro and their colleagues at theNational Opinion Research Center. The symbols were the ones used in theFIG. 2.1 and refered to the poll variant plotted. When only the monthwas available, the poll date was assigned to be the middle of the month.

Poll questions:

POLL VARIANT NBC1: Source: NBC News, 30 Rockefeller Plaza, New York,N.Y. 10020. Question: Do you think that the defense budget for next yearshould be increased, decreased or should it be kept the same as it isnow? Responses: (1) Increased, (2) Kept the same as now, (3) Decreased,(4) Not sure.

POLL VARIANT NBC2: Source: NBC News. Question: Do you think the federalgovernment's spending next year on defense and the military should beincreased, decreased, or kept about the same? Responses: (1) Increased,(2) Kept about the same, (3) Decreased, (4) Not sure.

POLL VARIANT GSS: Source: General Social Survey, National OpinionResearch Center, 6030 Ellis Ave., Chicago, Ill. 60637. Question: We arefaced with many problems in this country, none of which can be solvedeasily or inexpensively. I'm going to name some of these problems, andfor each one I'd like you to tell me whether you think we're spendingtoo much money on it, too little money, or about the right amount. Themilitary, armaments and defense. Responses: (1) Too little, (2) Aboutright, (3) Too much, (4) Don't know.

POLL VARIANT ROPER: Source: Roper Center for Public Opinion Resarch,University of Connecticut. Question and responses: Identical to thosefor variant GSS above.

Table B.2

ABC News Poll on the stationing of American troops in Lebanon. Resultsfrom ABC News Poll, 7 West 66th Street, New York, N.Y. 10023; Report 95in 1984 compiled with the aid of B. I. Page, R. Y. Shapiro and theircolleagues at the National Opinion Research Center. The question was:Would you say the U.S. should send more troops to Lebanon, leave thenumber about the same, or remove the troops that are there now? Theresponses were: Send More Troops; Leave Number the Same; Remove TroopsThere Now; No Opinion.

Table B.3

ABC News Poll on the Democratic Primary. Results from ABC News Pollcompiled with the aid of B. I. Page, R. Y. Shapiro and their colleaguesat the National Opinion Research Center. (see Table B.2). This questionwas asked to registered voters who identify themselves either asDemocrats or as independents who lean toward the Democrats: Imagine yourstate holds a Democratic primary and these are the candidates: ReubinAskew, Alan Cranston, John Glenn, Gary Hart, Ernest Hollings, JesseJackson, George McGovern and Walter Mondale. Whether you are a Democrator not, for whom would you vote: Askew, Cranston, Glenn, Hart, Hollings,Jackson, McGovern or Mondale? (slight variation of wording starting Sep.26, 1983). The responses for Mondale, Glenn, and No Opinion weretabulated separately. All other opinions were pooled and includedvolunteered responses for other minor candidates and for those who saidthey would not vote. This last catergory was 1-2% in all polls.

Table B.4

ABC News Poll on the Economic Climate. Results from ABC News Pollcompiled with the aid of B. I. Page, R. Y. Shapiro and their colleaguesat the National Opinion Research Center. (see Table B.2) The questionwas: Do you think the nation's economy is: Getting better; Gettingworse; Staying the same; No opinion.

Table B.5

NBC News Poll on the Importance of Unemployment vs. Inflation. Resultsfrom NBC News Poll compiled with the aid of B. I. Page, R. Y. Shapiroand their colleagues at the National Opinion Research Center. (see TableB.1). The question was: In your opinion which is the more importantproblem facing the country today--finding jobs for people who areunemployed or holding down inflation? The responses were: Finding jobs;Both equal; Holding down inflation; Not sure.

Table B.6

Polls on the desirability of sending Contra aid. Results from thepolling organizations indicated. All data from the Roper Center forPublic Opinion Research, P.O. Box 440, Storrs, Conn. 06268 and CBS News,524 W. 57th St., New York, N.Y. 10019. The question wordings by questionare:

1. (President Reagan has taken a number of steps in Central America tomeet what he says is the mounting supply of arms from Russia and Cubagoing to left-wing rebel forces in El Salvador and to the Sandinistagovernment in Nicaragua.) Let me ask you if you favor or oppose armingand supporting the rebels in Nicaragua who are trying to overthrow theSandinista government in that country? Favor; Oppose; Not sure.

2. Do you favor or oppose the U.S. arming and supporting the rebels inNicaragua who are trying to overthrow the Sandinista government in thatcountry? Favor; Oppose; Not sure.

3. (Now let me read you some statements about President Reagan'shandling of foreign affairs. For each, tell me if you agree ordisagree.) (Interviewer- Rotate Question Order) . . . It is wrong forthe CIA (Central Intelligence Agency) to help finance theanti-Sandinista forces in Nicaragua? Agree; Disagree; Not sure.

4. Do you favor or oppose . . . The U.S. (United States) arming andsupporting the rebels in Nicaragua, who are trying to overthrow theSandinista government in that country? Favor; Oppose; Not sure.

5. Should the United States be giving assistance to the guerrilla forcesnow opposing the Marxist government in Nicaragua? Yes; No; Don't know.

6. President Reagan recently asked Congress to authorize $100 million inU.S. aid to the rebels seeking to overthrow the communist government inNicaragua, including $70 million for military purposes and $30 millionfor non-military purposes, such as food and medical supplies. Do youthink the Congress should or should not authorize this new aid package?Should authorize (includes 2% volunteering should authorize non-militaryonly); Shouldn't authorize; No opinion.

7. The House of Representatives has refused Reagan's request for 100million dollars in military and other aid to the contra rebels inNicaragua. Do you approve or disapprove of that action by the House?Approve; Disapprove; Don't know/No opinion.

8. Do you favor or oppose the U.S. sending $100 million in military andnon- military aid to the Contra rebels in Nicaragua? Favor; Oppose; Notsure.

9. Do you think the U.S. government should give $100 in military andother aid to the Contras trying to overthrow the government inNicaragua? Yes, should; No, shouldn't; No opinion.

                  TABLE B. 1                                                      ______________________________________                                        Polls on the desirability of increasing defense spending.                     Symbol                                                                        & poll           Percent respnse                                              source    Date       (1)    (2)    (3)  (4)                                   ______________________________________                                          GSS     03/ /77    23.6   45.4   22.9 8.1                                   ˜ ROPER                                                                           12/07/77   23     40     24   13                                      GSS     03/ /78    27.0   43.6   21.8 7.6                                     NBC1    10/17/78   28     45     21   6                                     ˜ ROPER                                                                           12/06/78   31     35     23   11                                      NBC1    12/12/78   24     47     22   7                                     ˜ ROPER                                                                           02/05/79   41     35     16   9                                       NBC1    09/ /79    38     36     16   10                                      NBC1    12/12/79   51     31     9    9                                       NBC1    01/18/80   63     21     8    8                                       NBC1    01/30/80   69     19     5    7                                       GSS     03/ /80    56.3   25.7   11.5 6.5                                     NBC2    01/22/81   65     23     6    6                                       NBC2    02/ /81    63     25     8    4                                       NBC2    11/17/81   34     47     14   5                                     ˜ ROPER                                                                           12/09/81   29     38     27   7                                       GSS     03/ /82    29.4   35.8   30.1 4.7                                     NBC2    03/30/82   24     47     25   4                                     ˜ ROPER                                                                           12/08/82   19     37     38   6                                       GSS     03/ /83    24.1   37.8   32.5 5.6                                     NBC2    01/ /84    23     46     26   5                                       GSS     03/ /84    17.3   41.2   38.1 3.5                                   ______________________________________                                    

                  TABLE B. 2                                                      ______________________________________                                        ABC News Poll on the stationing of American troops in                         Lebanon.                                                                                Send    Leave            No                                         Date      More    Same      Remove Opinion                                    ______________________________________                                        09/26/83  7       48        40     5                                          10/23/83  21      21        48     10                                         10/25/83  31      26        39     5                                          10/27/83  17      36        42     5                                          11/07/83  13      41        39     7                                          12/13/83  9       38        48     5                                          01/03/84  5       30        59     6                                          01/04/84  8       29        57     6                                          01/17/84  7       31        58     4                                          ______________________________________                                    

                  TABLE B. 3                                                      ______________________________________                                        ABC News Poll on the Democratic Primary                                       Date      Mondale  Glenn     Others                                                                              No Opinion                                 ______________________________________                                        06/19/83  42       28        24    6                                          08/01/83  43       28        23    6                                          09/26/83  36       26        26    11                                         11/07/83  47       21        19    9                                          12/13/83  44       23        24    7                                          01/15/84  51       11        19    18                                         01/17/84  45       22        27    5                                          02/15/84  55       13        21    9                                          ______________________________________                                    

                  TABLE B. 4                                                      ______________________________________                                        ABC News Poll on the Economic Climate                                         Date      Better  Same      Worse No Opinion                                  ______________________________________                                        03/06/81   9      36        54    2                                           05/20/81  14      36        49    1                                           09/20/81  12      44        42    2                                           10/18/81  17      41        40    2                                           11/22/81  11      22        55    1                                           12/12/82  12      32        54    2                                           01/30/82  17      31        50    2                                           03/08/82  13      27        59    1                                           04/25/82  21      30        47    2                                           08/17/82  17      31        50    2                                           09/13/82  21      33        45    1                                           10/11/82  21      28        48    3                                           12/18/82  20      26        52    1                                           01/22/83  18      36        46    1                                           03/02/83  39      39        21    1                                           04/12/83  37      40        21    2                                           05/15/83  43      39        17    1                                           06/19/83  36      42        20    2                                           08/01/83  50      30        19    0                                           09/26/83  44      35        20    1                                           11/07/83  44      36        20    1                                           12/13/83  46      31        20    2                                           01/17/84  49      31        19    1                                           ______________________________________                                    

                  TABLE B. 5                                                      ______________________________________                                        NBC News Poll on the Imiportance of Unemployment vs.                          Inflation                                                                     Date    Unemployment                                                                              Equal     Inflation                                                                            Not Sure                                 ______________________________________                                        03/22/77                                                                              43          18        37     2                                        04/26/77                                                                              41          14        43     2                                        08/03/77                                                                              50          11        36     3                                        03/22/78                                                                              39          10        49     2                                        05/02/78                                                                              32           9        56     3                                        06/28/78                                                                              33          10        55     2                                        08/08/78                                                                              28          11        59     2                                        09/20/78                                                                              27           9        61     3                                        11/14/78                                                                              22           8        69     1                                        12/12/78                                                                              22           9        68     1                                        03/20/79                                                                              23          11        64     2                                        09/11/79                                                                              21          10        67     2                                        05/29/80                                                                              30          15        52     3                                        07/09/80                                                                              30          15        53     2                                        08/06/80                                                                              35          14        48     3                                        08/23/80                                                                              26          20        53     1                                        ______________________________________                                    

                  TABLE B. 6                                                      ______________________________________                                        Polls on the desirability of sending Contra aid.                              Poll                                                                          Number &       Percent response                                               Source    Date     Favor     Oppose                                                                              Don't Know                                 ______________________________________                                        1 HARRIS  08/20/83 66        23    11                                         2 HARRIS  09/12/83 60        24    16                                         3 HARRIS  07/10/84 55        32    13                                         4 HARRIS  03/04/85 53        36    11                                         5 GALLUP  08/28/85 58        29    13                                         6 GALLUP  03/07/86 52        37    11                                         7 ABC     03/22/86 60        35     4                                         8 HARRIS  04/07/86 62        33     5                                         9 CBS     04/08/86 62        25    13                                         ______________________________________                                    

SECTION C Summaries of Text Analyses

C.1 Strategy for Content Analysis by Successive Filtrations

Text was first processed through a series of "filter" program runs toremove irrelevant material. Finally the remaining, fairly homogeneoustext was scored for its support of each of the polled positions. Theoutline of these steps is provided in Chapter 3. This section summarizesthe dictionaries and rules used for the six analyses in this book. Alldispatches were given infon content scores corresponding to thepositions for which poll data were available (data in Section B). Toillustrate, a single dispatch is followed in detail through all theanalytic steps for the defense spending example.

C.2 Text Analysis for Defense Spending--Including Detailed Example

1. Filtration to select for Dispatches on American Defense Spending

The first step was a filtration to discard all dispatches not directlyrelevant to American defense spending. The entire text was marked forwords referring to America (denoted by {A}), defense (denoted by {D}),and spending (denoted by {S}). Articles with all three word classes wereretained for further analysis unless they also had the word "aid" or"fund" which led to the story being rejected as discussed in Chapter 3,Section 3.4-1.

The following actual dispatch, dated Feb. 19, 1983, was kept since ithad many America, defense and spending words and neither of theprohibited words "aid" or "fund:"

SECTION: Washington Dateline

LENGTH: 576 words

BYLINE: By MAUREEN SANTINI, Associated Press Writer

DATELINE: WASHINGTON

KEYWORD: {A}Reagan-{D}Defense

President {A}Reagan, invoking the menace of Adolf Hitler, asked{A}Congress on Saturday to suppress the urge to reduce his "minimal"1984 {D}military {S}budget.

In his weekly radio broadcast from the White {A}House, the presidentsaid his $238.6 billion {D}defense {S}spending proposal for fiscal year1984, which begins Oct. 1, was necessary "unless we're willing to gamblewith our immediate security and pass on to future generations the legacyof neglect we inherited.

"That kind of neglect would only weaken peace and stability in theworld, both now and in the years ahead."

{A}Reagan said," Now, I know this is a hard time to call for increased{D}defense {S}spending. It isn't easy to ask {A}American families whoare already making sacrifices in the recession . . .

"On the other hand, it's always very easy and very tempting politicallyto come up with arguments for neglecting {D}defense {S}spending in timeof peace," the President said.

"One of the great tragedies of this century was that it was only afterthe balance of power was allowed to erode and a ruthless adversary,Adolf Hitler, deliberately weighed the risks and decided to strike thatthe importance of a strong {D}defense was realized too late."

Though {A}Reagan called for an overall freeze on domestic {S}spending inhis 1984 {S}budget, the {D}defense portion increased by 14 percent. Andthat was after the president cut $8 billion from the Pentagon {S}requestbefore submitting it to {A}Congress.

{A}Reagan said he and his administration had "agonized" over the current{D}defense {S}budget by trimming {S}requests and cutting non-essentialprograms.

"The {D}defense {S}budget we finally presented is a minimal {S}budget toprotect our country's vital interests and meet our commitments," hesaid.

The president said it was "far better to prevent a crisis than to haveto face it unprepared at the last moment. That's why we have anoverriding moral obligation to invest now, this year, in this {S}budget,in restoring {A}America's strength to keep the peace and preserve ourfreedom."

He said the Soviet Union outspends the {A}United States on . . . fitsand starts," he said, "we will never convince the Soviets that it's intheir interests to behave with restraint and negotiate genuine {D}armsreductions. We will also burden the {A}American taxpayer time and againwith the high {S}cost of crash rearmament.

"Sooner or later, the bills fall due."

{A}Senate Minority Leader Robert C. Byrd of West Virginia gave theDemocratic response to Reagan's comments and took issue with thepresident's contention that cutting the administration {D}defense{S}budget {S}request would expose the country to danger.

"For example, we do not need two new manned {D}bombers--one of whichwill be obsolete almost immediately after it is built," Byrd said,referring to the B-1 {D}bomber under construction and the advancedStealth plane expected to emerge from development late in this decade.

Arguing that the national {D}defense depends on a strong economy, Byrdstressed the need for greater . . .

Three adjacent dots in the text indicated that the remainder of asentence was not retrieved due to the text being further than 50 wordsfrom one of the seven search words: defense military, arms, spend!,expenditure, fund!, and budget! (see Section B).

After this filtration, the total number of characters of text droppedfrom 820,000 to 600,000. The average number of words per dispatchincreased from 148 to 199. The dispatch number dropped more dramatically(from 692 to 377) than the number of characters of text because verylittle was retrieved from irrelevant dispatches. The collection wasstopped as soon as a story was seen to be not pertinent during theretrieval from the Nexis data base. The increase in average word countper dispatch was a natural consequence of discarding dispatches fromwhich very few words were collected.

2. Filtration to select for paragraphs on defense spending.

The second text analysis step selected only paragraphs directlydiscussing defense spending. The condition was that a defense word(denoted by {D}) be close to a spending word (denoted by {S}). Theparagraphs from the dispatch given above were scored using these rules.The decision for each paragraph is given directly below the paragraph:

President Reagan, invoking the menace of Adolf Hitler, asked Congress onSaturday to suppress the urge to {S}reduce his "minimal" 1984{D}military {S}budget.

ABOVE PARAGRAPH WAS KEPT.

In his weekly radio broadcast from the White House, the president saidhis $238.6 billion {D}defense {S}spending proposal for fiscal year 1984,which begins Oct. 1, was necessary "unless we're willing to gamble withour immediate security and pass on to future Generations the legacy ofneglect we inherited.

ABOVE PARAGRAPH WAS KEPT.

"That kind of neglect would only weaken peace and stability in theworld, both now and in the years ahead."

ABOVE PARAGRAPH WAS DISCARDED.

Reagan said, "Now, I know this is a hard time to call for {S}increased{D}defense {S}spending. It isn't easy to ask American families who arealready making sacrifices in the recession . . .

ABOVE PARAGRAPH WAS KEPT.

"On the other hand, it's always very easy and very tempting politicallyto come up with arguments for neglecting {D}defense {S}spending in timeof peace," the president said.

ABOVE PARAGRAPH WAS KEPT.

"One of the great tragedies of this century was that it was only afterthe {S}balance of power was allowed to erode and a ruthless adversary,Adolf Hitler, deliberately weighed the risks and decided to strike thatthe importance of a strong {D}defense was realized too late."

ABOVE PARAGRAPH WAS DISCARDED.

Though Reagan called for an overall freeze on domestic {S}spending inhis 1984 {S}budget, the {D}defense portion {S}increased by 14{S}percent. And that was after the president{S} cut $8 billion from the{D}Pentagon {S}request before submitting it to Congress.

ABOVE PARAGRAPH WAS KEPT.

Reagan said he and his administration had "agonized" over the current{D}defense {S}budget by trimming {S}requests and {S}cuttingnon-essential programs.

ABOVE PARAGRAPH WAS KEPT.

"The {D}defense {S}budget we finally presented is a minimal {S}budget toprotect our country's vital interests and meet our commitments," hesaid.

ABOVE PARAGRAPH WAS KEPT.

The president said it was far better to prevent a crisis than to have toface it unprepared at the last moment. "That's why we have an overridingmoral obligation to invest now, this year in this {S}budget, inrestoring america's {S}strength to keep the peace and preserve ourfreedom."

ABOVE PARAGRAPH WAS DISCARDED.

He said the soviet union outspends the united states on . . .

ABOVE PARAGRAPH WAS DISCARDED.

. . fits and starts, "he said. "We will never convince the soviets thatit's in their interests to behave with restraint and negotiategenuine{D} arms {S}reductions. We will also burden the American taxpayertime and again with the high{S} cost of crash rearmament.

ABOVE PARAGRAPH WAS KEPT.

"Sooner or later, the bills fall due."

ABOVE PARAGRAPH WAS DISCARDED.

Senate Minority Leader Robert C. Byrd of West Virginia gave theDemocratic response to Reagan's comments and took issue with thepresident's contention that {S}cutting the administration {D}defense{S}budget {S}request would expose the country to danger.

ABOVE PARAGRAPH WAS KEPT.

"For example, we do not need two new manned {D}bombers, one of whichwill be obsolete almost immediately after it is built," Byrd said,referring to the B-1 {D}bomber under construction and the advancedstealth {S}plane expected to emerge from development late in thisdecade."

ABOVE PARAGRAPH WAS DISCARDED.

Arguing that the national {D}defense depends on a strong economy, Byrdstressed the need for greater . . .

ABOVE PARAGRAPH WAS DISCARDED

This sample dispatch was chosen because it illustrated most of thefeatures of the text analysis. In consequence, this story was one of themost complex found and was somewhat atypical in containing a substantialamount of information indirectly relevant to the topic defense spending.More frequently, the discarded text was on a topic other than defensespending. In a press conferences, for instance, the shift in topic couldbe quite abrupt.

Nevertheless, the relevant thoughts in the discarded text, even in theabove example, were almost always also found in the retained text. Forexample, the first discarded paragraph was an expansion on the point inthe previous, retained paragraph rather than being a new idea. Also, thenext to the last discarded paragraph only illustrated the point in theprevious, retained paragraph.

Although a small amount of relevant information may have been lost bydiscarding the paragraphs with pertinent information which was indirect,the gain was the immense simplification of the subsequent analysis withthe total text from all dispatches being reduced from 600,000 charactersto 220,000.

3. Numerical scoring for three positions on defense spending.

The paragraphs retained from the second filtration described above werethen scored for favoring more, same or less defense spending. Since theprevious filtration had already guaranteed that a defense word was closeto a spending word, the scoring only depended on a defense word (denotedby {D}) being close to modifiers implying these three positions. Themodifiers fell into the three classes favoring more (denoted by {M}),same (denoted by {S}), and less (denoted by {S}) with a less word closeto a more word being equivalent to a same word and with a less wordclose to another less word also being equivalent to a same word. In somecombinations, word order and proximity were both also important. Inaddition, the prefix "non" (denoted by {n}) preceding a defense wordmeant that the defense word was not considered to be relevant to themilitary.

All paragraphs had a total score of 1.0 with each cluster of modifierwords close to a defense word contributing to the final score. If aparagraph only had one such cluster, the entire paragraph score of 1.0was assigned to the appropriate position. When there was more than onecluster, the score of 1.0 was divided into equal fractions with eachcluster receiving one part.

This scoring procedure is illustrated using the retained paragraphs ofthe dispatch considered above. The score for each paragraph is givenimmediately following the paragraph. The scores were for whether theparagraph favored more, same and/or less defense spending. The indentedtext following the scores explains way the computer arrived at thedecisions.

President Reagan, invoking the menace of Adolf Hitler, asked Congress onSaturday to {L}suppress the urge to {L}reduce his "minimal" 1984{D}military budget.

SCORE FAVORING: More=0.00 Same=1.00 Less=0.00

The "suppression" of a "reduction" implied favoring the same level forthe "military" budget. The three words were treated as a cluster becausethey were close to each other.

In his weekly radio broadcast from the White House, the president saidhis $238.6 billion {D}defense spending proposal for fiscal year 1984,which begins Oct. 1, was {M}necessary unless we're willing to gamblewith our immediate security and pass on to future generations the legacyof {M}neglect we inherited.

SCORE FAVORING: More=0.00 Same=0.00 Less=0.00

No score here. "Necessary" and "neglect" which implied more spendingwere too far away from "defense."

Reagan said, "Now, I know this is a hard time to call for {M}increased{D}defense spending. It {L}isn't easy to ask American families who arealready making sacrifices in the recession . . .

SCORE FAVORING: More=1.00 Same=0.00 Less=0.00

The operative word combination was "increased" "defense." The word"isn't" only changed the sense of words like "increased" if theincreases was ahead of the isn't.

"On the other hand, it's always very easy and very tempting politicallyto come {M}up with arguments for {M}neglecting {D}defense spending intime of peace," the president said.

SCORE FAVORING: More=1.00 Same=0.00 Less=0.00

The scored combination was "up" . . . "neglecting" "defense". "Up" and"neglect" were scored together as meaning more should be spent. Theinclusion of words like "neglect" and "inadequate" in the dictionary didpermit the public to reason and thereby take indirect information intoaccount. These words were included because they were usually found inthe context of arguments that defense spending should have beenincreased if it was neglected or inadequate.

Though Reagan called for an overall {S}freeze on domestic spending inhis 1984 budget, the {D}defense portion {M}increased by 14 percent. Andthat was after the president {L}cut $8 billion from the {D}Pentagonrequest before submitting it to Congress.

SCORE FAVORING: More=0.50 Same=0.00 Less=0.50

This paragraph was scored as making two different statements onefavoring more defense spending ("defense" "increased") and one favoringless ("cut" "pentagon"). Therefore, the paragraph score of 1.0 wasdivided in two. "Freeze" was too far away from "defense" to have aconnotation for defense, as was consistent with the actual meaning ofthe paragraph.

Reagan said he and his administration had "agonized" over the current{D}defense budget by {L}trimming requests and {L}cutting{n}non-essential programs.

SCORE FAVORING: More=0.00 Same=1.00 Less=0.00

The reasonable score favoring unchanged military spending wasserendipitous. This score was due to "trimming" and "cutting" beingequivalent to the concept of same spending. This combination close to"defense" gave the score favoring same spending. The "non" did not havea function here. If the word defense occurred in place of the wordessential, then the concept of defense would have been nullifiedindicating that the topic was not about defense.

"The {D}defense budget we finally presented is a minimal budget toprotect our country's vital interests and meet our commitments," hesaid.

SCORE FAVORING: More=0.00 Same=0.00 Less=0.00

This paragraph had no score since "defense" was close to no modifierwords. In fact, when this paragraph was read by itself, it wasconsistent with any of the positions. The actual information favoringone position or another was elsewhere in the text.

. . fits and starts," he said. We will never convince the Soviets thatit's in their interests to behave with restraint and negotiate genuinearms {L}reductions. We will also burden the American taxpayer time andagain with the {M}high cost of crash rearmament.

SCORE FAVORING: More=0.00 Same=0.00 Less=0.00

This paragraph also had no score since there were not words directlyconnoting defense. It could be argued that it favored more defensespending indirectly. However, the statement was probably weaker thanthose above actually speaking directly to the issue.

Senate Minority Leader Robert C. Byrd of West Virginia gave theDemocratic response to Reagan's comments and took issue with thepresident's contention that {L}cutting the administration {D}defensebudget request would expose the country to {L}danger.

SCORE FAVORING: More=0.00 Same=0.00 Less=1.00

The score of favoring less spending came from "cutting" "defense.""Danger" was too far from "defense" to be scored. The score was probablycorrect although a sounder basis for the conclusion would have included:took issue . . . cutting . . . defense . . . danger.

The final score for this dispatch was 2.5 paragraphs favoring more, 2.0favoring same and 1.5 favoring less spending.

This sample dispatch was one of the most complex retrieved. Six of thenine paragraphs were scored for supporting one of the three positions.For comparison, the average number of relevant paragraphs was only 1.7among all dispatches with at least one paragraph with a positive score.

Since the average AP paragraph had approximately 30 words, the finalscoring came from approximately 50 words although approximately 80 wordswere examined in each scored dispatch.

The difference between the 50 and 80 words meant that 30-40% of theparagraphs had no score. This was true for 3 of the 9 paragraphs in thedispatch just examined.

The scoring for defense spending came from paragraphs representing 5-10%of the words in the average dispatch with 400-900 words. Only 20% of thedispatches had fewer words and 10% had more.

Being fairly long, this dispatch also illustrated most of the scoringfeatures. In fact, the problems were more severe for this text than formost others. The more typical dispatches had smaller numbers of relevantparagraphs and usually made their points about defense spending quitedirectly before proceeding to other topics. There tended to be fewercrosscurrents to complicate the scoring.

The most appropriate base for considering the scoring was the 377dispatches retained after the first filtration step. The others were notabout defense spending or were about non-American forces. Of these 377,72% were used in the final scoring.

4. Numerical scoring for two positions on Defense Spending

As an alternative to the evaluation just described, the text was alsoscored to favor only two positions-more or less defense spending. To doso, the concept of same spending was eliminated. As a result, somemodifier words like "maintain" and "keep" were omitted from thedictionary. These words were previously interpreted to favor the conceptof same spending. Other words, like "freeze" and "frozen" were movedfrom the same spending class to the modifier class connoting lessspending. Now, a less word preceding a more word was assigned to favorless instead of same spending (e.g. "cut . . . increase"). Similarly,two nearby less words (e.g. "cut . . . reduction") were also assigned tofavor more instead of same.

Other dictionary changes included the deletion of a few words favoringmore (bolster) and less (alternate, weaken, without) spending. The wordsnuclear and arms were added to the list of words referring to defense.Thus "nuclear arms reduction talks" was interpreted to support lessdefense spending while this phrase was simply ignored in the previousscoring.

Using this alternate dictionary and its associated rules, the textscored in Section C.3-3 above was rescored. Those paragraphs withchanged final scores are listed below together with indented comments:

President Reagan, invoking the menace of Adolf Hitler, asked Congress onSaturday to {L}suppress the urge to {L}reduce his "minimal" 1984{D}military budget.

SCORE FAVORING: More=1.00 Less=0.00

The "suppress" . . . "reduce" was interpreted previously to favor samespending instead of more spending.

Reagan said he and his administration had "agonized" over the current{D}defense budget by {L}trimming requests and {L}cutting{n}non-essential programs.

SCORE FAVORING: More=1.00 Less=0.00

The "trimming" . . . "cutting" was misscored previously to favor samespending and was misscored this time to favor more spending. Again, thewrong score was not entirely inconsistent with the sense of theparagraph.

. . fits and starts," he said. "We will never convince the Soviets thatit's in their interests to behave with restraint and negotiategenuine{D} arms {L}reductions. We will also burden the American taxpayertime and again with the {M}high cost of crash rearmament."

SCORE FAVORING: More=0.00 Less=1.00

Previously, this paragraph had a zero score since "arms" was not in thedictionary in order to omit reference to arms reduction. Here, "arms""reduction" was interpreted to suggest that defense spending, likewise,should be diminished.

With these modifications, the new score was 4.5 paragraphs favoring morespending and 2.5 favoring less. The text recoveries during the scoringare presented in Table C.1.

5. Text analysis for defense waste and fraud.

a. Filtration to remove dispatches not on American defense spending

The first step discarded dispatches if they were not about the Americanwaste and fraud. The U.S. was usually not the focus when there was amention of a non-American region in the heading portion prepared by theAP and listed before the body of a dispatch. The heading contained thesedesignators: dateline, headline, keyword and section (see example inSection C.3-1 above). Therefore, the filtration command was simply tolook for one of these designators followed closely by a word referringto foreign part of the world. Dispatches referring to defense againstwaste and fraud for non-defense topics were also not retained forfurther study if the stories mentioned other key words such as hazardousand toxic referring to non-military waste.

b. Filtration to select paragraphs on defense waste and fraud.

This filtration was accomplished by looking for word combinationsreferring to both the defense industry and to waste. Some combinationswere simple, like "overcharge" . . . "weapons." Other combinations weremore complex, like "defense" . . . "contractor" . . . "cut corners."

c. Numerical Scoring for stories on defense waste and fraud.

Any word combination suggesting defense waste such as those in theprevious paragraph led to the AP paragraph containing the combination tobe scored as favoring less defense spending. The recovery data for thiswaste and fraud analysis is given in Table C.2.

C.3 Text Analysis for Troops in Lebanon

1. Filtration to select for paragraphs on American troops in Lebanon.

The first filtration selected paragraphs containing words referring toAmerica, troops, and Lebanon. At this step, a mention of policy or asynonym was considered to be equivalent to troops since policy oftenreferred to troops. Paragraphs were discarded if they had wordsreferring to non-American troops (e.g. Arab, Christian, Druse, Syrian,Israeli), a non-Lebanon region of the world (e.g. Grenada), or had wordsreferring to non-military activities (e.g. economy). In this analysis,paragraphs were considered to be about America or Lebanon after aprevious mention of words indicating these geographic areas unless therewas a word (e.g. Christian, Syrian, France) indicating eithernon-American troops, a non-Lebanese location (.e.g. Israel). Also,pronouns such as "they" and "them" were taken to refer to troops ifthere was a mention of troops in the previous paragraph.

2. Filtration to select remove paragraphs on military action andChristmas entertainment.

This step removed all text on actual combat and all paragraphs onentertainer Bob Hope's Christmas visit to Lebanon.

3. Numerical scoring for dispatches on troops in Lebanon.

The final scoring step used the major criterion that a word referring totroops or policy should be near modifiers word favoring more, same orless although some words--like stay and withdraw--were able bythemselves to favor keeping or removing troops. Therefore, theparagraphs had infon content scores favoring more, same or less troops.The recovery data for the analyses are given in Table C.3.

C.4 Text Analysis for the Democratic Primary

1. Analysis using bandwagon words

a. Filtration for paragraphs about candidates. First, paragraphs wereselected and kept only if they had at least one of the names of theDemocratic candidates appearing in the ABC News Poll of Table B.3.

b. Scoring using bandwagon words. Then the text was scored for beingeither favorable or detrimental to John Glenn, Walter Mondale, or Others(Rubin Askew, Alan Cranston, Gary Hart, Ernest Hollings, Jesse Jackson,or George Mcgovern). These scores depended on modifier words implyingsuccess or failure being close to a candidate name. The scores belongedto 6 positions, 3 favorable to Mondale, Glenn, and Others, and 3disadvantageous to these candidates. The recovery data are in Table C.4.

2. Analysis using Name Count

The paragraphs in the original retrievals were scored without anyprevious filtration steps. Every paragraph was still given a total scoreof 1.0. If only one candidate was mentioned in the paragraph, then thescore in favor of that candidate was 1.0. If several candidates werediscussed, then that score of 1.0 was shared among the candidates. Thistype of scoring obviously did not generate any scores unfavorable to acandidate. Therefore, only three types of paragraph scores wereobtained, those mentioning Mondale, Glenn and others (recovery data inTable C.4).

C.5 Text Analysis for the Economic Climate

1. Filtration to eliminate dispatches on non-American economies.

The first step discarded dispatches if they were not about the UnitedStates. The procedure was very similar to that in Section C.2-5a above,looking for non-U.S. words in the the dispatch heading region.

2. Filtration to select paragraphs discussing the economy.

The next filtration step selected paragraphs with at least one wordreferring to some aspect of the economy. The reference did not have tobe to the economy as whole but could include components such as"agriculture." Also permitted were words like "rally" describingeconomic performance.

3. Numerical Scoring

In the final step, infon content scores were assigned from single wordssuggesting better, same or worse in the context of economic conditions.Therefore, the dictionary had qualifiers like "best", "confusion" and"bad" divided into classes favoring better, same and worse. Additionaldictionary words included those (e.g. "not" and "difficult") which couldalter the sense of the qualifier words. Since the previous filtrationhad already guaranteed that each paragraph had to make reference to anaspect of the economy, the score could be determined by single wordssuggesting better, same or worse. The recovery data for all steps aregiven in Table C.5.

C.7 Text Analysis for Unemployment versus Inflation

1. Filtration to eliminate dispatches on non-American economies.

This step to remove dispatches on foreign countries was like the firstfiltration for dispatches on defense waste and fraud (Section C.2-5a)and the economic climate (previous section).

2. Numerical scoring

The scoring was for: unemployment more important, equal importance, orinflation more important. The main criterion was that inflation orunemployment or a synonym should be close to a modifier words indicatingthat the problem was important. A significant number of paragraphs spokeof both problems being important. In recognition of this fact, the scorewas for equal importance if a modifier word made inflation important andif an unemployment word followed shortly after as in the phrase "we mustcombat both inflation and unemployment." Similarly, if a paragraph hadone word cluster supporting the importance of each of the two topics,the problems were considered to be equally crucial (recovery data inTable C.6).

C.7 Text Analysis for Contra Aid

1. Filtration to select paragraphs on Contra aid.

Paragraphs kept in this filtration step required that there besimultaneous mention of a word implying Nicaragua, the United States,and funding. For this filtration step it did not matter if the referenceto Nicaragua was to the Contras opposing the government or to thegovernment side. A mention of Nicaragua meant that the next paragraphalso was about Nicaragua unless there was discussion of another CentralAmerican country like El Salvador or Honduras.

2. Numerical scoring by Fan.

The paragraphs surviving the filtration were scored by the author bylooking for modifier words close to word combinations discussing boththe Contras and funding. Combinations of modifier words were examinedfor whether they favored or opposed Contra aid. An example of a wordcluster favoring Contra aid would be "approve" . . . "Contra" . . ."aid." If there were conditional words like "if" in the paragraph, theparagraph was considered to favor both positions equally. Therefore, thefinal scores were for paragraphs either supporting or opposing Contraaid.

3. Numerical scoring by Simone French, Peter Miene, and Janet Swim.

The paragraphs scored by Fan were scored independently by these threegraduate research assistants working as a team. Their scoring method wasquite different from that of Fan looking at word combinations favoringor opposing aid without requiring that these words be close to wordsdiscussing Contra aid. French et al included many indirect pieces ofinformation to imply a position on Contra aid. For instance, the wordcluster "administration" . . . "propaganda" by itself was scored asopposing Contra aid. This could safely be done because the paragraphswere already scored as being relevant to Contra aid by the filtration.

The recovery data for both scoring methods are given in Table C.7.

Table C.1

Summary of Text Analysis for Defense Spending. The upper portion givesthe recoveries of the text and paragraphs at different stages of thetext analysis. The Nexis search identified 9314 dispatches of which 692were retrieved at random. For the calculation, words are assumed to beapproximately 8 characters long. The lower portion gives data for eachposition scored after the final step in the upper portion of the table.The data for Any position refers to all positions combined.

Table C.2

Summary of Text Analysis for Defense Waste and Fraud: Scoring of allmentions of waste and fraud as favoring less defense spending. See TableC.1 for explanation. The Nexis search identified 878 dispatches of which487 were retrieved at random.

Table C.3

Summary of Text Analysis for Troops in Lebanon: Scoring for more, sameand less troops. See Table C.1 for explanation. The Nexis searchidentified 1517 dispatches of which 467 were retrieved at random.

Table C.4

Summary of Text Analysis for Democratic Primary: Scoring for bandwagonwords. See Table C.1 for explanation. The Nexis search identified 2435dispatches of which 425 were retrieved at random.

Table C.5

Summary of Text Analysis for Economic Climate: Scoring for better, sameand worse. See Table C.1 for explanation. The Nexis search identified12,393 dispatches of which 461 were retrieved at random.

Table C.6

Summary of Text Analysis for Unemployment versus Inflation: Scoring forunemployment, equal and inflation more important. See Table C.1 forexplanation. The Nexis search identified 1591 dispatches of which 1582were retrieved at random.

Table C.7

Summary of Text Analysis for Contra Aid: Scoring for infons favoring andopposing aid. See Table C.1 for explanation. The Nexis search identified1156 dispatches of which 969 were retrieved at random.

                                      TABLE C. 1                                  __________________________________________________________________________    Summary of Text Analysis for Defense Spending.                                __________________________________________________________________________    Step in          Characters of text                                                                     Dispatches                                                                            8 Char. Words                               Analysis         No. % Orig.                                                                            No.                                                                              % Orig.                                                                            per Dispatch                                __________________________________________________________________________    Nexis Retrieval  820,000                                                                           100  692                                                                              100  148                                         First Filter     600,000                                                                           73   377                                                                              54   199                                         Second Filter    220,000                                                                           27   340                                                                              49    81                                         Scoring Runs:                                                                 Scored to Favor More, Same, Less                                                                        272                                                                              39                                               Scored to Favor More and Less                                                                           280                                                                              40                                               only                                                                          __________________________________________________________________________                     Average paragraphs in                                                         dispatches with at least                                                                  Total dispatches                                 Position         one paragraph favoring                                                                    with a least one paragraph                       favoring         this position                                                                             favoring this position                           __________________________________________________________________________    Scored to Favor More, Same, Less:                                             More             1.3         177                                              Same             1.1          66                                              Less             1.2         132                                              Any position     1.7         272                                              Score to Favor More and Less                                                  Only:                                                                         More             1.3         197                                              Less             1.3         167                                              Any position     1.7         280                                              __________________________________________________________________________

                  TABLE C. 2                                                      ______________________________________                                        Summary of Text Analysis for Defense Waste and Fraud: Scoring                 of all mentions of waste and fraud as favoring less defense                   spending.                                                                     ______________________________________                                        Stop in                                                                              Characters of text                                                                         Dispatches   8 Char. Words                                Analysis                                                                             No.     % Orig.  No.   % Orig.                                                                              per Dispatch                             ______________________________________                                        Nexis  660,000 100      512   100    160                                      Retrieval                                                                     First  350,000 54       279   54     167                                      Filter                                                                        Second  83,000 13       159   31      69                                      Filter                                                                        Scored to               147   29                                              Mention                                                                       Waste                                                                         and                                                                           Fraud                                                                         ______________________________________                                                Average paragraphs in                                                         dispatches with at least                                                                      Total dispatches with                                 Position                                                                              one paragraph favoring                                                                        at least one paragraph                                favoring                                                                              this position   favoring this position                                ______________________________________                                        Less due to                                                                           1.3             147                                                   waste and                                                                     fraud                                                                         ______________________________________                                    

                                      TABLE C.3                                   __________________________________________________________________________    Summary of Text Analysis for Troops in Lebanon: Scoring for                   more, same and less troops.                                                   __________________________________________________________________________    Step in            Characters of text                                                                       Dispatches                                                                            8 Char. Words                           Analysis           No.   % Orig.                                                                            No.                                                                              % Orig.                                                                            per Dispatch                            __________________________________________________________________________    Nexis Retrieval    1,570,000                                                                           100  467                                                                              100  420                                     First Filter       490,000                                                                             31   393                                                                              89   156                                     Second Filter      240,000                                                                             15   352                                                                              80   85                                      Scored to Favor More, Same and Less                                                                         238                                                                              54                                           __________________________________________________________________________                Average paragraphs in                                                         dispatches with at least                                                                        Total dispatches                                Position    one paragraph favoring                                                                          with at least one paragraph                     favoring    this position     favoring this position                          __________________________________________________________________________    More        0.6                36                                             Same        1.6               197                                             Less        1.4               172                                             Any position                                                                              2.4               238                                             __________________________________________________________________________

                                      TABLE C. 4                                  __________________________________________________________________________    Summary of Text Analysis for Democratic Primary: Scoring for                  bandwagon words.                                                              __________________________________________________________________________    Step in     Characters of text                                                                      Dispatches                                                                             8 Char. Words                                  Analysis    No.  % Orig.                                                                            No. % Orig.                                                                            per Dispatch                                   __________________________________________________________________________    Nexis Retrieval                                                                           1,100,000                                                                          100  425 100  310                                            Bandwagon Analysis:                                                           First Filter                                                                                610,000                                                                          100  425 100  199                                            Scoring Run           159  37                                                 Name Count analysis:  425 100                                                 __________________________________________________________________________                Average paragraphs in                                                         dispatches with at least                                                                   Total dispatches                                                 one paragraph favoring                                                                     with at least one paragraph                          Position    this position                                                                              favoring this position                               __________________________________________________________________________    Bandwagon Analysis:                                                           Pro Mondale 1.6          85                                                   Pro Glenn   1.2          29                                                   Pro Others  1.4          56                                                   Con Mondale 1.1          16                                                   Con Glenn   1.9          12                                                   Con Others  1.1          33                                                   Any position                                                                              2.0          158                                                  Name Count Analysis:                                                          Mention Mondale                                                                           2.6          240                                                  Mention Glenn                                                                             2.0          187                                                  Mention Others                                                                            4.2          326                                                  Any position                                                                              5.6          425                                                  __________________________________________________________________________

                  TABLE C. 5                                                      ______________________________________                                        Summary of Text Analysis for Economic Climate: Scoring for                    better, same and worse.                                                       ______________________________________                                        Step in Characters of text                                                                         Dispatches  8 Char. Words                                Analysis                                                                              No.     % Orig.  No.  % Orig.                                                                              per Dispatch                             ______________________________________                                        Nexis   730,000 100      461  100    197                                      Retrieval                                                                     First Filter                                                                          590,000 81       367  80     201                                      Second  420,000 58       366  79     144                                      Filter                                                                        Scored to                306  66                                              Favor                                                                         Better,                                                                       Same and                                                                      Worse                                                                         ______________________________________                                                Average paragraphs in                                                         dispatches with at least                                                                      Total dispatches with                                 Position                                                                              one paragraph favoring                                                                        at least one paragraph                                favoring                                                                              this position   favoring this position                                ______________________________________                                        Better  2.0             245                                                   Same    0.7              10                                                   Worse   1.8             222                                                   Any     3.0             306                                                   position                                                                      ______________________________________                                    

                                      TABLE C. 6                                  __________________________________________________________________________    Summary of Text Analysis for Unemployment versus Inflation:                   Scoring for unemployment, equal and inflation more important.                 __________________________________________________________________________    Step in              Characters of text                                                                      Dispatches                                                                             8 Char. Words                         Analysis             No.  % Orig.                                                                            No. % Orig.                                                                            per Dispatch                          __________________________________________________________________________    Nexis Retrieval      2,300,000                                                                          100  1582                                                                              100  177                                   First Filter         1,800,000                                                                           79  1183                                                                              75   189                                   Scored to Favor Unemployment Important,                                                                       695                                                                              44                                         Equal Importance, and Inflation Important                                     __________________________________________________________________________    Position     Average paragraphs in                                            favoring     dispatches with at least                                                                       Total dispatches                                importance   one paragraph favoring                                                                         with at least one paragraph                     of           this position    favoring this position                          __________________________________________________________________________    Unemployment 1.4              281                                             Equal        1.1              243                                             Inflation    1.4              442                                             Any position 1.7              695                                             __________________________________________________________________________

                                      TABLE C. 7                                  __________________________________________________________________________    Summary of Text Analysis for Contra Aid: Scoring for infons                   favoring and opposing aid.                                                    __________________________________________________________________________    Step in          Characters of text                                                                      Dispatches                                                                            8 Char. Words                              Analysis         No.  % Orig.                                                                            No.                                                                              % Orig.                                                                            per Dispatch                               __________________________________________________________________________    Nexis Retrieval  2,000,000                                                                          100  969                                                                              100  258                                        First Filter     1,300,000                                                                           63  920                                                                              95   164                                        Scored to Favor and Oppose Aid:                                               by Fan                     770                                                                              84                                              by Swim et al              906                                                                              98                                              __________________________________________________________________________                 Average paragraphs in                                                         dispatches with at least                                                                     Total dispatches                                               one paragraph favoring                                                                       with at least one paragraph                       Position     this position  favoring this position                            __________________________________________________________________________    Fan Analysis:                                                                 Favor Aid    1.2            585                                               Oppose Aid   1.3            620                                               Either Position                                                                            2.0            770                                               Swim et al Analysis:                                                          Favor Aid    1.7            770                                               Oppose Aid   2.2            837                                               Either Position                                                                            3.4            906                                               __________________________________________________________________________

SECTION D Details of Actual Public Opinion Projections

The basic method for computing public opinion from AP infons wasoutlined in Section A (Section A.10) and involved: (1) constructing"skeleton persuasive force" functions G" from the persistance constant,infon content scores and infon emission times, (2) formulating a"population conversion model" describing the opinion conversions due topersuasive messages, and (3) using functions G", the populationconversion model, the "modified persuasibility constants" and an initialset of poll values to compute public opinion. The calculations for theinfon content scores have already been presented in Section C. ThisSection describes the calculations of the persuasive forces drivingopinion changes and the subsequent projections of public opinion.

D.1 Computations of Persuasive Forces

The equations for the calculations are those given in Section A. Theassumptions specific to computations for AP dispatches are given inSection A.10.

First, all the infons favoring a position were pooled and their dates oftransmission and content scores were used for computing skeletonpersuasive force functions G"_(j) (t) using postulated values for thepersistence constant and Equation A.27 of Section A. Unless otherwisestated, this persistence constant was assigned a one day half-life forall infons.

D.2 Population Conversion Models

These models, all presented as figures in Chapter 4 (e.g. FIG. 4.2),specify the indices over which the summations are performed in EquationA.26. Every model has one subpopulation corresponding to each of thepoll positions. The subpopulation names always begin with B (for"believers" in a position). The other elements in the models are thepersuasive force functions (or the skeleton persuasive force functionsG" since these two types of functions differ only by a constant ofproportionality). The model describes the opinion changes resulting fromthe actions of individual peruasive force functions.

D.3 Opinion Projections

After formulation of population conversion models like those in FIG.1.2, opinion computations were made from Equation A.26. The computationsbegan with the time and opinion percentages at the first measured pollpoint. Then calculations were made for opinion one @t later using theopinion projection equations for all the subpopulations. The result wasopinion predictions for all positions at the new time. These valueswhere then used for further calculations after another @t. The processwas repeated until the time of the last poll point. Along the way,estimates were obtained for all the intermediate poll points for all thepoll positions.

For these calculations, the @t was usually chosen to be a few hours.This @t was known since it was chosen by the investigator. The T/R ratiowas also known since that was only the reciprocal of the fraction of APdispatches studied among the total identified. The unknowns required inprojection equations are:

1. The persistence constant. For each of the six examples, this constantwas optimized by calculating the mean squared deviations (MSD) (seeSection A, section A.10) between the calculated opinion and opinion asmeasured in polls. The optimum persistence constant was the one givingthe lowest MSD. Since a one day half-life was a good concensus value forthis constant (Chapter 4), this value was used for all plottedprojections.

2. The modified persuasibility constant k'_(2j) assuming the samek'_(2j) for all positions. Formally, it was conceivable this constantcould be different for each position for which there were infons(Section A). However, in initial calculations, it was assumed that allpositions had the same k'_(2j). The best value for this common k'_(2j)was found by minimization of the MSD.

3. Refining weights for infons favoring different positions. Once thebest common k'_(2j) was assigned, the projections were examined to seeif expected opinion for any one position was consistently too high ortoo low. If so, then a minimum MSD calculation was performed to see if ak'_(2j) different from the common k'_(2j) gave a lower MSD. If asignificantly lower MSD was observed, then the altered k'_(2j) was usedfor the projections. However, this difference was hoe reported as a newk'_(2j) but rather as a refining weight defined as the ratio between thenewly chosen constant and the common k'_(2j) optimized in Point 2 above.This refining weight specified the relative persuasiveness of thedifferent groups of infons. Therefore, all modified persuasibilityconstants were given as the product of the constant assigned in Point 2above and a corresponding refining weights.

Whenever any of the constants discussed above was optimized, all otherconstants were retested to see if they still gave the best value. Ifnot, all were reoptimized until the best combination of constants wasobtained. The only exception was for Contra aid where a one daypersistence half-life was used since this was the best concensusconstant for all issues.

Although the invention has been described here in its preferred form, itshall be understood that changes may be made in detail in structurewithout departing from the spirit and scope of the invention as setforth in the claims appended hereto.

I claim:
 1. A system for determining the presence within a text messageof one or more predetermined ideas wherein the text of said message isin digital form and includes components in a human language, said systemcomprising:a) means for searching said message for a plurality ofpredetermined scoring words and identifying words in said message whichmatch with said scoring words; b) means for determining the sequencewithin said message of matching scoring words and the distance betweencertain ones of said matching scoring words; and c) means foridentifying which said ideas are present in said message according toone or more predetermined rules, each of said rules specifying arelationship between one or more of said matching scoring words.
 2. Asystem according to claim 1 further including means for assigningnumerical scores for said message according to said one or morepredetermined rules.
 3. A system according to claim 1 further includingcommunication means for electronically connecting to an electronic database containing text stored in digital form and for obtaining said text.4. A system according to claim 1, wherein said system furtherincludes:d) means for dividing the text of said message into specifiedblocks of text, wherein said blocks can include all or a subset of thetext of said message; e) means for making summary representations ofeach said block of text wherein each important concept conveyed by saidmessage is reduced to a single concept symbol, and wherein eachimportant concept is identified from:1) one or more specified wordswithin said block of text; 2) a quantity of text between said one ormore specified words within said block of text; 3) the sequence withinsaid message of said one or more specified words; and f) means foraltering specified blocks of text.
 5. The system according to claim 4further wherein said system further includes means for determining,based on said summary representations, scores for each said messagerepresenting the presence and absence of text favoring said ideas. 6.The system according to claim 4 wherein said means for making summaryrepresentations further includes:b)(1) a dictionary containing a limitednumber of words likely to be contained in the text of said specifiedblocks of text; b)(2) a set of concept symbols corresponding to theconcepts to be used in evaluating the presence of said ideas within saidspecified blocks of text; b)(3) means for assigning each word in saiddictionary to one of said concept symbols; b)(4) means for scanning thetext of said specified block of text using pattern matching for theoccurrence of matching words wherein matching words are those also foundin said dictionary; b)(5) a set of transformation rules for identifyingsaid important concepts within said text wherein an important concept isidentified from:(i) one or more specified words within said specifiedblock of text, (ii) a quantity of text between said one or morespecified words within said specified block of text, and b)(6) means fortransforming said summary representations to obtain a reduced summaryrepresentations wherein said important concepts conveyed by said text,relevant to said ideas, are each reduced to a single concept symbol. 7.The system according to claim 6 wherein each said reduced summaryrepresentation is in the form of a text equivalent listingcomprising:b)(6)(i) a symbol representing a beginning of said specifiedblock of text wherein said symbol has the same form as that of saidconcept symbols; b)(6)(ii) a distance symbol representing a quantity oftext between the beginning of said specified block of text and its nextnearest neighbor wherein said next nearest neighbor is a said matchingword, or the end of said block of text; b)(6)(iii) the concept symbolcorresponding to each said matching word in said specified block oftext; b)(6)(iv) a distance symbol corresponding to said distance betweeneach said matching word and its next nearest neighbor or said end ofsaid specified block of text; and b)(6)(v) sequence informationspecifying the sequence of said beginning, each said matching word, andsaid end of said specified block of text.
 8. The system according toclaim 6 wherein said means for transforming said summary representationsto obtain said reduced summary representations further includes:b)(6)(i)means for performing evaluations of pairs of said concept symbols insaid text equivalent of said specified block of text wherein saidevaluations are based on said symbols, said sequence information, andsaid distance symbols in said text equivalent listing; and b)(6)(ii)means for performing transformations on said text equivalent listing toreduce said text equivalent listing to important concept symbols whereinsaid transformations are based on said evaluations of pairs of saidconcept symbols.
 9. The system according to claim 8 wherein said meansfor performing transformations further includes, based on relationshipsdefined in said transformation rules:b)(6)(ii)(A) means for inserting aspecified distance symbol and a specified concept symbol into saidspecified text equivalent listing; and b)(6)(ii)(B) means for modifyingsaid sequence information of said specified text equivalent listing. 10.The system according to claim 8 wherein said means for performingtransformations further includes, based on relationships defined in saidtransformation rules:b)(6)(ii)(A) means for deleting a specified conceptsymbol and a specified distance symbol from a specified text equivalentlisting; and b)(6)(ii)(B) means for modifying said sequence informationof said text equivalent.
 11. The system according to claim 4 whereinsaid means for making summary representations includes:b)(1) means foraltering said summary representations based on a said specified block oftext and specified input rules, wherein said input rules specify one ormore concept symbols to insert and the method for insertion there into;and b)(2) means for altering said specified block of text, based onother blocks of text and specified input rules, wherein said input rulesspecify one or more words to insert and locations within said individualtext blocks for their insertion.
 12. The system according to claim 11further including means for insertion of one or more specified wordsinto the text of said message at locations specified by said input rulesand based on said summary representation, wherein said one or morespecified words are quantities of text defined in said input rules. 13.The system according to claim 11 further including means for deletionsof above specified words from the text of said message at locationsspecified by said input rules and based on said summary representation,wherein said specified words are quantities of text defined in saidinput rules.
 14. The system according to claim 11 further includingmeans for replacement of one or more specified words in the text of saidmessage by one or more specified replacement words at locationsspecified by input rules, based on said summary representation, whereinsaid one or more specified words and said one or more replacement wordsare quantities of text defined in said input rules.
 15. A system forreducing a text message into its essential message components andidentifying the presence within said message of one or morepredetermined ideas wherein the text of said message is stored indigital form and includes components in a human language, said systemcomprising: computer means including:a) a listing of predeterminedconcept categories and a dictionary of predetermined identifying wordswherein each of said identifying words is a text representationcorresponding to one of said concept categories, and wherein saidconcept categories represent a predetermined concept; b) a set ofpredetermined text analysis rules wherein said text analysis rulesdefine relationships between one or more concept categories; c) meansfor dividing the text of said message into specified blocks of text,wherein said blocks can include all or a subset of the text of saidmessage; d) means for searching each said block of text for a firstplurality of words in said message which match with said predeterminedidentifying words, such instance of a plurality being a set of matchingwords; e) means for determining the sequence of said matching words insaid block of text and the distance between pairs of said matching wordswherein distance is a numeric representation of the quantity of textbetween the said pair of matching words in said text; f) means foranalyzing said matching words, said sequence of matching words, and saiddistances between pairs of matching words to select blocks of text insaid message according to one or more of said text analysis rules,wherein said text analysis rules define a relationship between one ormore matching words that identifies said blocks of text as relevant to asaid idea; g) means for searching each said relevant block of text insaid message for a plurality of words which match with saidpredetermined identifying words; h) means for determining the sequencewithin said block of text of matching identifying words and the distancebetween each pair of matching identifying words; and i) means fordetermining within each relevant block of text using said predeterminedtext analysis rules, the quantities of text favoring each of said ideas.16. A system according to claim 15 wherein said computer means furtherincludes means for assigning numerical scores for each relevant block oftext according to said predetermined text analysis rules, each of saidrules further specifying a relationship between said concept categorieseach corresponding to one said matching identifying word and identifyingwhich said ideas the block of text favors.
 17. A system according toclaim 16 wherein said computer means further includes means forassigning a numerical score to said message according to the numericalscores for the relevant blocks of text found therein.
 18. A systemaccording to claim 15 further including communication means forelectronically connecting said computer means to an electronic data basecontaining text stored in digital form and for transferring said text tosaid computer means.
 19. The system according to claim 15 wherein saidcomputer means further includes:j) means for making summaryrepresentations of said specified blocks of text wherein each importantconcept conveyed by said block of text is reduced to a single conceptsymbol, and wherein each important concept is identified from:(i) one ormore specified words within said specified block of text; (ii) aquantity of text between said one or more specified words within saidblock of text; and (iii) the sequence within said specified block oftext of said one or more specified words; and k) means for alteringspecified blocks of text.
 20. The system of claim 19 further includingmeans for determining, based on said summary representations:1) whichblocks of text to use to determine said numerical scores for each saidmessage; and 2) numerical scores for each said message.
 21. The systemaccording to claim 19 wherein said means for making said summaryrepresentations from said specified blocks of text furtherincludes:a)(1) means for altering said summary representations based onsaid specified blocks of text and specified input rules, wherein saidinput rules specify one or more concept symbols to insert and the methodfor insertion there into; and a)(2) means for altering individual saidspecified blocks of text, based on other blocks of text and specifiedinput rules, wherein said input rules specify one or more words toinsert and locations within said individual text blocks for theirinsertion.
 22. The system according to claim 21 further including meansfor insertion of one or more specified words into the text of saidmessage at locations specified by said input rules and based on saidsummary representation, wherein said one or more specified words arequantities of text defined in said input rules.
 23. The system accordingto claim 21 further including means for deletions of above specifiedwords from the text of said message at locations specified by said inputrules and based on said summary representation, wherein said specifiedwords are quantities of text defined in said input rules.
 24. The systemaccording to claim 21 further including means for replacing one or morespecified words in the text of said message by one or more specifiedreplacement words at locations specified by input rules, based on saidsummary representation, wherein said one or more specified words andsaid one or more replacement words are quantities of text defined insaid input rules.
 25. The system according to claim 19 wherein saidmeans for making summary representations of said specified blocks oftext further includes:j)(1) a dictionary containing a limited number ofwords likely to be contained in the text of said specified blocks oftext; j)(2) a set of concept symbols corresponding to the concepts to beused in evaluating presence of said ideas within said specified blocksof text; j)(3) means for assigning each word in said dictionary to oneof said concept symbols; j)(4) means for scanning the text of saidspecified block of text using pattern matching for the occurrence ofmatching words wherein matching words are those also found in saiddictionary; j)(5) a set of transformation rules for identifying saidimportant concepts within said text wherein an important concept isidentified from:(i) one or more specified words within said specifiedblock of text, (ii) a quantity of text between said one or morespecified words within said specified block of text, and j((6) means fortransforming said summary representations to obtain a reduced summaryrepresentations wherein said important concepts conveyed by said text,relevant to said ideas, are each reduced to a single concept symbol. 26.The system according to claim 25 wherein each said reduced summaryrepresentation is in the form of a text equivalent listingcomprising:j)(6)(i) a symbol representing a beginning of said specifiedblock of text wherein said symbol has the same form as that of saidconcept symbols; j)(6)(ii) a distance symbol representing a quantity oftext between the beginning of said specified block of text and its nextnearest neighbor wherein said next nearest neighbor is a said matchingword, or the end of said block of text; j)(6)(iii) the concept symbolcorresponding to each said matching word in said specified block oftext; j)(6)(iv) a distance symbol corresponding to said distance betweeneach said matching word and its next nearest neighbor or said end ofsaid specified block of text; and j)(6)(v) sequence informationspecifying the sequence of said beginning, each said matching word, andsaid end of said specified block of text.
 27. The system according toclaim 25 wherein said means for transforming said summaryrepresentations to obtain said reduced summary representations furtherincludes:j)(6)(i) means for performing evaluations of pairs of saidconcept symbols in said text equivalent of said specified block of textwherein said evaluations are based on said symbols, said sequenceinformation, and said distance symbols in said text equivalent listing;and j)(6)(ii) means for performing transformations on said textequivalent listing to reduce said text equivalent listing to importantconcept symbols wherein said transformations are based on saidevaluations of pairs of said concept symbols.
 28. The system accordingto claim 27 wherein said means for transforming further includes, basedon relationships defined in said transformation rules:j)(6)(i) means forinserting a specified distance symbol and a specified concept symbolinto said specified text equivalent; and j)(6)(ii) means for modifyingsaid sequence information of said specified text equivalent.
 29. Thesystem according to claim 27 wherein said means for transforming furtherincludes, based on relationships defined in said transformationrules:j)(6)(i) means for deleting a specified concept symbol and aspecified distance symbol from a specified text equivalent; andj)(6)(ii) means for modifying said sequence information of said textequivalent.
 30. The system according to claim 19 wherein said computermeans further includes:(l) means for altering said summaryrepresentations based on said specified blocks of text and specifiedinput rules, wherein said input rules specify one or more conceptsymbols to insert and the method for insertion there into; and (m) meansfor altering individual said specified blocks of text, based on otherblocks of text and specified input rules, wherein said input rulesspecify one or more words to insert and locations within said individualtext blocks for their insertion.
 31. The system according to claim 30further including means for insertion of one or more specified wordsinto the text of said message at locations specified by said input rulesand based on said summary representation, wherein said one or morespecified words are quantities of text defined in said input rules. 32.The system according to claim 30 further including means for deletionsof above specified words from the text of said message at locationsspecified by said input rules and based on said summary representation,wherein said specified words are quantities of text defined in saidinput rules.
 33. The system according to claim 30 further includingmeans for the replacement of one or more specified words in the text ofsaid message by one or more specified replacement words at locationsspecified by input rules, based on said summary representation, whereinsaid one or more specified words and said one or more replacement wordsare quantities of text defined in said input rules.
 34. A system fordetermining the presence within a text message of one or morepredetermined ideas wherein the text of said message is in digital formand includes components in a human language, said system comprising:a)means for searching said message for a plurality of predeterminedscoring words and identifying words in said message which match withsaid scoring words; b) means for determining the sequence within saidmessage of matching scoring words and the distance between certain onesof said matching scoring words; and c) means for identifying which saidideas are present in said message according to one or more predeterminedrules, each of said rules specifying a relationship between one or moreof said matching scoring words;said means further including: d) meansfor dividing the text of said message into specified blocks of text,wherein said blocks can include all or a subset of the text of saidmessage; e) means for making summary representations of each said blockof text wherein each important concept conveyed by said message isreduced to a single concept symbol, and wherein each important conceptis identified from:1) one or more specified words within said block oftext; 2) a quantity of text between said one or more specified wordswithin said block of text; 3) the sequence within said message of saidone or more specified words; and f) means for altering specified blocksof text.
 35. A system for determining the presence within a text messageof one or more predetermined ideas wherein the text of said message isin digital form and includes components in a human language, said systemcomprising:a) means for searching said message for a plurality ofpredetermined scoring words and identifying words in said message whichmatch with said scoring words; b) means for determining the sequencewithin said message of matching scoring words and the distance betweencertain ones of said matching scoring words; c) means for transformingsaid message to make a summary representation thereof; d) means foridentifying which said ideas are present in said message according toone or more predetermined rules, each of said rules specifying arelationship between one or more of said matching scoring words.
 36. Thesystem according to claim 35 wherein said transformation reduces saidmessage to a concept symbol.
 37. A system for reducing a text messageinto its essential message components and identifying the presencewithin said message of one or more predetermined ideas wherein the textof said message is stored in digital form and includes components in ahuman language, said system comprising: computer means including:a) alisting of predetermined concept categories and a dictionary ofpredetermined identifying words wherein each of said identifying wordsis a text representation corresponding to one of said conceptcategories, and wherein each said concept category represents apredetermined concept; b) a set of predetermined text analysis ruleswherein said text analysis rules define relationships between one ormore concept categories; c) means for dividing the text of said messageinto specified blocks of text, wherein said blocks can include all or asubset of the text of said message; d) means for searching each saidblock of text for a first plurality of words in said message which matchwith said predetermined identifying words, such instance of a pluralitybeing a set of matching words; e) means for determining the sequence ofsaid matching words in said block of text and the distance between pairsof said matching words wherein distance is a numeric representation ofthe quantity of text between the said pair of matching words in saidtext; f) means for analyzing said matching words, said sequence ofmatching words, and said distances between pairs of matching words toselect blocks of text in said message according to one or more of saidtext analysis rules, wherein said text analysis rules define arelationship between one or more matching words and identify relevantblocks of text as relevant to said ideas; g) means for searching eachsaid relevant block of text in said message for a plurality of wordswhich match with said predetermined identifying words; h) means fordetermining the sequence within said block of text of matchingidentifying words and the distance between each pair of matchingidentifying words; i) means for transforming said block of text to makea summary representation thereof; j) means for determining within saidrelevant block of text using said predetermined text analysis rules, thequantities of text favoring each of said ideas.
 38. The system accordingto claim 37 wherein said transformation reduces each block of text to aconcept symbol.